IRIS Authentication Accuracy â€“ PoC Report - uidai

UIDAI 

h 

Unique Identification Authority of India 

Planning Commission, Govt. of India (GoI), 

3rd Floor, Tower II, 

Jeevan Bharati Building, 

Connaught Circus, 

New Delhi 110001 

Role of Biometric 

Technology in Aadhaar 

Authentication 

IRIS Authentication Accuracy – PoC Report 

Date: 14 September 2012

Executive Summary 

UIDAI has collected iris images of residents during enrollment, and 

successfully leveraged iris modality for de-duplication. A Proof of 

Concept (PoC) study was carried out to assess the feasibility of using 

iris for on-line biometric authentication in the Indian context. The 

findings of this study are presented in this report. 

Iris Authentication Accuracy - PoC Report 

This report covers the design framework, field implementation, data 

collection and analysis. It interprets empirical results to assess Figure 1 Young resident at POC 

effectiveness of iris technology, authentication processes and devices. It concludes with a set of 

observations and findings for iris authentication process and device ecosystem. 

Assessment of iris feasibility can be further divided into 

1. Coverage: Can residents use iris efficiently and conveniently for Aadhaar authentication? 

2. Accuracy: How accurately can iris authenticate a resident? 

3. Device readiness: Are there commercially available devices capable of performing 

authentication? 

4. System readiness: Is the entire authentication system ready for performing iris 

authentication? 

Study Process: PoC was conducted in a manner similar to the previous study [UIDAI, FP, 2012]. 

Residents with Aadhaar number were asked to participate in the test. In the first part, scenario test, 

each resident was authenticated on-line on one of the eight cameras. In the second part, technology 

test, the resident’s iris images were captured on all eight cameras. In both parts, extensive logs 

were generated for off-line analysis. During the analysis, the data was first reviewed and cleansed of 

Feasibility of Iris based on-line Authentication 

The empirical results clearly demonstrate iris authentication to be viable in 

Indian context. 

Findings 

% of Residents 

Single-eye cameras Dual-eye cameras 

Authenticated in first try 95.89 99.29 

Authenticated in multiple tries 99.21 99.40 

Failed authentication 

(FRR+FTC) 

0.79 0.60 

Device Readiness: Six different devices with a variety of form and function are 

available to form competitive vendor eco-system. 

System Readiness: Median time for end-to-end authentication was less than one 

minute over GPRS mobile network in semi-rural setting. 

© UIDAI, 2009-2012 Page 2 of 36

exceptions and anomalies. 

Set-up: The PoC was conducted in semi urban setting at 

centers in Nanjagud taluk in Mysore district of Karnataka 

between May 27 th and July 30 th 2012. The PoC centers 

were designed to resemble expected ground reality. No 

attempt was made to create an ideal situation. In online 

(scenario) testing, 17,990 online authentication 

transactions were performed by 5,747 residents. The 

online authentications were performed on a GPRS based 

mobile network. In the off-line (technology) study, 

5,833 residents went through 40,148 capture sessions 

on 8 authentication cameras. All the cameras used in 

the POC studies were obtained through an Expression of 

Interest (EOI). In all, 215,342 iris images were captured 

during this process. 


Iris PoC was carried out using the Aadhaar authentication system described in an earlier report 

[UIDAI, FP, 2012]. 

Results: 

The PoC was conducted to obtain sample that demographically represents Indian population. At the 

end, the sample used has a slight over representation of women and seniors (over 65 years) and 

under-representation of children (below 15 years) as compared to Aadhaar enrollment data. 

Coverage: The coverage achieved by iris authentication was high, demonstrating the potential of iris 

to be an inclusive modality. Out of 5,833 residents, only one resident was not able to use any of 

the eight cameras, and 18 additional residents (0.31%) could not use four or more cameras. 

Thus, there is no inherent technical or physiological limitation to using iris for authentication. It 

should be possible for 99.67% of population to conveniently attempt iris authentication. In 

other words, failure to capture (FTC) rate is in the range of 0.33% with current capture software. 

The study has identified the root cause and suggested methods to improve vendor’s capture 

algorithm to achieve even higher coverage through this report. 

Accuracy: Table 1 shows the True Accept Rate (TAR) for different authentication modes with two 

attempts averaged over six “good” cameras. The data shows that high accuracy can be obtained 

using both single and dual eye cameras. In summary, False Reject Rate (FRR) of less than 0.5% is 

achievable whenever two irises are matched on single or dual eye cameras. 

Authentication Mode Single eye camera Dual eye camera 

One iris authentication 96.21% Not Applicable 

Two irises authentication 99.54% 99.73% 

Table 1: True Accept Rate at FAR = 1e -6 

Accuracy insight and conclusions: 

a) Manual inspection of iris images provided insight into the root cause of failure to capture 

and false rejects cases. Over two-third residents in question had eye deformity arising from 

certain types of surgeries or eye conditions and hence some cameras were unable to 

capture the image which resulted in FTC. Others cameras were more lenient in capturing 

© UIDAI, 2009-2012 Page 3 of 36


the image but authentication failed. These residents with special eye condition will not be 

able to authenticate using irises because either the cameras will be unable to capture or 

even when captured, they may fail to authenticate successfully as the current capture and 

matcher software are unable to process them. With this situation clearly identified and 

communication with vendors in progress, future devices and matching algorithms could be 

enhanced to accommodate some of these condition further increasing iris accuracy. 

Additional study may be also needed to more accurately estimate and analyse eye surgery 

cases (particularly, cataract surgeries) across India. 

b) Fingerprints of these individuals with special eye conditions were reviewed. For most of the 

residents, it was estimated that their fingerprints are of sufficient quality to enable 

fingerprint based biometric authentication. 

c) It was observed that DET curve for iris is flat implying little deterioration in FRR rate with 

stringent FAR. This binary behaviour – either a good match or not a match – could be 

uniquely useful in situations requiring high level of assurance such as money transfer or 

secure access control. Conversely, running iris authentication at less stringent FAR (such as 1 

in 10,000) will not improve the FRR. 

d) Two iris authentication shows material improvement over one iris authentication. It is 

estimated that additional 3% residents could be reliably authenticated by expanding 

authentication to include two irises. For all age categories, accuracy continues to be higher 

than 98.94%. Among those children have performed best, followed by adults. Some of the 

seniors could not be authenticated due to special eye conditions. This indicates the need to 

tune the capture and matcher algorithms specifically for seniors to accommodate for special 

eye conditions. 

Device readiness: Six of the iris cameras participating in this study performed at required accuracy 

level. This will assure competitive vendor eco-system to the service providers willing to use iris 

authentication. Key variables determining effectiveness of a camera are 

a. Single eye and dual eye capture: Four cameras each of single eye and dual eye were tested. 

Both single eye and dual eye devices were found suitable for iris authentication. 

b. Capture aid: Various physical and optical aids were utilized by different cameras. Cameras 

with visual indications on the camera for capture process, eye alignment, actionable 

feedback and capture completion were materially easier to use. 

c. High Capture quality – In addition to quality algorithms being in the device to capture high 

quality images, vendors could incorporate specific suggestions made in this document to 

further enhance image quality. 

All participating camera vendors were asked to supply IRIS segmentation SDK which produces 

image in formats KIND 1, 2, 3 and 7 (also known as Uncropped, VGA, Cropped and Cropped & 

Masked respectively) compliant to ISO 19794-6 standard [ISO 19794-6, 2011]. The camera 

vendors provided predominantly only two formats, KIND 1 and KIND 2. The image size is a 

dominant factor in network latency for mobile networks. While this PoC used KIND 2 

compressed image of size 15 KB, use of KIND 7 would have reduced it to 2 KB resulting in 

speedier authentication. 

As a result of factors mentioned above, there was a difference in performance of different 

cameras. In each category of single and dual eye cameras, one camera was an outlier compared 

to median performance of the group. For example, one camera demonstrated a failure to 

© UIDAI, 2009-2012 Page 4 of 36


capture (FTC) rate of about 10% when the median of peer cameras was below 0.5%. Some of 

the outliers were also judged to be more difficult to use in the field and were not considered in 

accuracy analysis. Suggestions made in this report would improve performance of cameras, 

especially these outlier cameras. 

System readiness: The overall system behaved reliably and conveniently for both resident and 

operators. A few cases of system failure were observed. Two important metrics measure user 

friendliness of the iris based authentication. 

a. Authentication time. Using a GPRS based semi-rural network, median time across all devices 

was less than one minute. Authentication time is defined as time taken to complete full 

transaction including capture of two irises, local image quality check, operator review, 

transmission, backend processing and authentication response. 

b. Round trip time. Less than a quarter of the authentication cycle was transmission and 

backend matching time. Median round trip was less than 10 seconds, while only 7.8% of 

transactions took more than 15 seconds. 

Previous PoC demonstrated that the current setup is capable of supporting sustained fingerprint 

authentication speeds of one million per hour. It has been demonstrated repeatedly in the 

literature that iris matcher is more efficient than fingerprint matcher. Therefore no independent 

tests were performed using iris matcher. Based on published data, it is safe to state that Aadhaar is 

capable of sustaining iris authentication rate of over one million per hour. However UIDAI will do 

benchmarking of t its backend system to validate the peak load scenarios for iris authentication also. 

Findings and Next Steps 

The empirical results clearly demonstrate iris authentication to be viable in Indian context. With 

current level of device readiness for iris capture, it is capable of providing coverage for 99.67% of 

population with authentication accuracy of above 99.5% using two irises and up to two attempts. 

Suggestions made in this document for the vendors, once implemented, will improve the rates 

further. The overall systems – network and software - have shown to meet desired requirements in 

real life condition. Finally, six different devices with variety of form and function are available to 

provide competitive vendor eco-system. 

The PoC also provided a number of insights that could further improve performance of device and 

system. Three key improvements are 

a. Improvement in matcher algorithm and device capture software to handle residents with 

special eye conditions. 

b. Support for KIND 7 image format by device vendor to help reduce image size during 

transmission to about 2 KB from the current 15KB. 

Several ergonomic improvements in device design to aid in easier and more accurate 

capture. These cover visual aids for proper placement of camera and appropriate visible 

light source inside certain types of camera. 

UIDAI would be working with the biometric ecosystem to implement the findings mentioned in this 

document and take up further field studies to fine-tune iris authentication technology. These studies 

would also lead to formulation of iris device specifications which will be used for certification by 

STQC. 

© UIDAI, 2009-2012 Page 5 of 36

Abbreviations 

API Application Programming Interface 

ASA Authentication Service Agency 

AUA Authentication User Agency 

EOI Expression of Interest 

CIDR Central Identity Data Repository 

DET Detection Error Tradeoff 

NIST National Institute of Standards and Technologies 

IREX Iris Exchange 

FAR False Accept Rate 

FRR False Reject Rate 

FTC Failure To Capture 

IEC Information, Education and Communication 

KYC Know Your Customer 

PDS Public Distribution System 

PoC Proof of Concept 

SDK Software Development Kit 


STQC Standardisation Testing and Quality Certification Directorate 

TAR True Accept Rate (1 – FAR) 

UIDAI Unique Identification Authority of India 

© UIDAI, 2009-2012 Page 6 of 36


Contents 

Executive Summary ................................................................................................................................. 2 

Abbreviations .......................................................................................................................................... 6 

1 Aadhaar Authentication ................................................................................................................ 10 

2 Objective ....................................................................................................................................... 11 

3 Design and Field Implementation ................................................................................................. 12 

3.1 Introduction to PoC studies .................................................................................................. 12 

3.2 Iris Authentication Concepts ................................................................................................. 12 

3.3 Test Methodology ................................................................................................................. 13 

3.4 Resident and operator communication and training............................................................ 17 

3.5 Data Quality & Data Analysis ................................................................................................ 18 

4 Feasibility of using iris for authentication ..................................................................................... 19 

4.1 Demographic Profile ............................................................................................................. 19 

4.2 Coverage ............................................................................................................................... 20 

4.3 Accuracy results from technology test ................................................................................. 21 

4.4 System findings ..................................................................................................................... 24 

4.5 Devices Findings .................................................................................................................... 25 

4.6 Other findings ....................................................................................................................... 27 

5 Observations & Findings ............................................................................................................... 28 

5.1 Coverage ............................................................................................................................... 28 

5.2 Accuracy ................................................................................................................................ 28 

5.3 Device Readiness .................................................................................................................. 28 

5.4 System Readiness .................................................................................................................. 29 

5.5 Findings ................................................................................................................................. 29 

5.6 Way Forward ......................................................................................................................... 30 

6 References .................................................................................................................................... 31 

7 Appendix – Camera related findings ............................................................................................. 32 

7.1 Capture distance and Capture Volumes ............................................................................... 32 

7.2 Image Capture and Output ................................................................................................... 32 

7.3 Capture aids .......................................................................................................................... 33 

7.4 Detection and Capture of iris ................................................................................................ 34 

7.5 Dual eye vs. single eye cameras ............................................................................................ 35 

7.6 Capture Distance ................................................................................................................... 36 

© UIDAI, 2009-2012 Page 7 of 36

Figures 


Figure 1 Young resident at POC .............................................................................................................. 2 

Figure 2 POC Center at Nanjangud ....................................................................................................... 12 

Figure 3 Residents waiting for their turn .............................................................................................. 12 

Figure 4 Resident at POC site ................................................................................................................ 15 

Figure 5 Screen shot of Client UI ........................................................................................................... 16 

Figure 7: IEC material used during POC ................................................................................................ 17 

Figure 6: Announcer .............................................................................................................................. 17 

Figure 8: Age distribution of participants in study compared to Aadhaar enrolment ......................... 19 

Figure 9: Gender distribution of participants in study compared to Aadhaar enrolment ................... 20 

Figure 10: ICCE Cataract surgery ........................................................................................................... 20 

Figure 11: Occlusion .............................................................................................................................. 21 

Figure 13 DET Curve - Single and Dual Iris Authentication ................................................................... 23 

Figure 12 FRR by matchers.................................................................................................................... 23 

Figure 14: Resident authentication time .............................................................................................. 24 

Figure 15: Network Latency histogram ................................................................................................. 24 

Figure 16: Image Formats ..................................................................................................................... 25 

Figure 17: Sample images of residents providing samples during POC ................................................ 26 

Figure 18: Age wise DET chart .............................................................................................................. 27 

Figure 19 Resident with special condition Eyes .................................................................................... 35 

© UIDAI, 2009-2012 Page 8 of 36

List of Tables 


Table 1: True Accept Rate at FAR = 1e -6 .................................................................................................. 3 

Table 2: Vendor Participation ............................................................................................................... 16 

Table 3: Failure to Capture ................................................................................................................... 20 

Table 4: Camera-wise FTC ..................................................................................................................... 21 

Table 5: Resident wise Accuracy ........................................................................................................... 22 

Table 6: Camera wise Reject Rates (2 irises with 2 attempts) .............................................................. 22 

Table 7: Single Eye Camera - Good and all single eye camera TAR ...................................................... 22 

Table 8: Dual eye camera - Good and all Dual eye camera TAR ........................................................... 23 

Table 9: True Accept Rate at FAR = 1e-6. ............................................................................................. 28 

Table 10 Camera Capture distance and capture volume information ................................................. 32 

© UIDAI, 2009-2012 Page 9 of 36

1 Aadhaar Authentication 


The Unique Identification Authority of India (UIDAI) has been created with the mandate of providing 

a Unique Identity (Aadhaar) to all residents of India. The CIDR processes these enrolments by deduplicating 

them to ensure uniqueness and then issues Aadhaar numbers. As of August 2012, nearly 

200 million residents have been enrolled in Aadhaar and over 195 million of them have been issued 

with Aadhaar numbers 

One of the mandates given to UIDAI is to define usages and applicability of Aadhaar for delivery of 

benefit services. The Aadhaar number, which uniquely identifies a resident, will give individuals 

means to clearly establish their identity to public and private agencies across the country for service 

delivery. UIDAI provides online authentication using the resident’s demographic and biometric 

information to support Aadhaar-enabled delivery of services. 

Aadhaar Authentication is the process wherein, Aadhaar number along with the Aadhaar holder’s 

personal identity data is securely submitted to the CIDR for matching, following which the CIDR 

verifies the correctness thereof on the basis of the match with the Aadhaar holder’s identity 

information available with it. To protect resident’s privacy, Aadhaar Authentication service responds 

only with a “Yes/No” and no Personal Identity Information (PII) is returned as part of the response. 

Aadhaar Authentication supports several different types of authentication through a combination of 

demographic fields, biometric fields as well as other methods such as one-time-password (OTP). In 

case of iris, the CIDR server supports authentication of iris images compliant with ISO standard that 

ensures vendor neutral, open format. 

For further information on Aadhaar authentication system, please refer to www.uidai.gov.in. 

This study analyzes the feasibility of using iris as a modality for authentication. 

© UIDAI, 2009-2012 Page 10 of 36

2 Objective 


The objective of the PoC was to determine the feasibility of using iris modality for on-line 

authentication. 

UIDAI has successfully leveraged iris modality in enrollment and has collected iris images of the 

resident during enrollment. Iris is used for biometric de-duplication. This study was aimed at 

assessing its use for biometric authentication in the Indian context. 

Iris as a biometric modality promises certain unique characteristics in Indian environment. Iris 

technology literature lists several benefits. 

1. The iris does not get worn out with age, or with use. In addition, iris authentication is not 

impacted by changes in the weather. 

2. Iris image capture does not require physical contact. Capturing iris image is physically similar to 

familiar practice of taking photographs. 

3. Iris capture requires simple instructions such as ‘look at the camera, keep your eyes wide open’. 

4. Fake iris is difficult to synthesize making it harder to impersonate. 

5. Iris image cannot be captured without resident cooperation. 

6. Cost and manufacturing of iris cameras have been positively affected by the spread of low cost 

consumer cameras. 

Few global initiatives have empirically published results on iris based online authentication in a 

context similar to Aadhaar. Therefore the goal for the study was also to 

Characterize & propose optimal authentication setups (server & device level) for online 

biometric authentication of residents using iris authentication device. 

To achieve the goal, it is necessary to conduct rigorous field tests on sample population and to 

measure feasibility in four areas: 

A. Coverage indicates the percentage of the population that is able to conveniently and efficiently 

utilize iris authentication. For any system to be utilized for large scale benefit delivery, ease of 

use and speed of verification are also key measure of the overall coverage. 

B. Accuracy measure percentage of time system is able to authenticate genuine resident while 

rejecting attempt by imposter. 

C. System readiness covers overall response time with particular emphasis on network latency, 

reliability, stability and ease of deployment 

D. Device readiness measures individual device’s ability to perform above three factors reliably. 

Particular attention was given to understand different device characteristics and develop 

recommendation for enhancing device performance. 

© UIDAI, 2009-2012 Page 11 of 36

3 Design and Field Implementation 

3.1 Introduction to PoC studies 

There have been few studies regarding biometric 

authentication in the Indian field context and it was 

important to conduct rigorous PoC studies in order to 

design and configure the UIDAI authentication system. A 

number of such PoC studies have been carried out, each 

building on the learning from the previous ones. This 

chapter describes the design and setup for this study. 

Continuing the methodology used in the previous study 

[UIDAI, FP, 2012], the iris PoC tests various parameters 

using [ISO 19795-2, 2007], which provides the following definitions: 


Figure 2 POC Center at Nanjangud 

Scenario evaluation is an online evaluation of end-to-end system performance in a prototype or 

simulated application. The utility of scenario testing stems from the inclusion of human-sensor 

acquisition interaction in conjunction with the enrolment and recognition processes, whose 

benefits include the following: 

� Ability to gauge impact of additional attempts and transactions. 

� Ability to collect throughput results for resident authentication. 

and 

Technology evaluation is the offline evaluation of one or more devices and algorithms for the 

same biometric modality using a corpus of samples. The utility of technology testing stems from 

its separation of the human-sensor acquisition interaction and the recognition process. It allows 

for varying different parameters to re-run the same sample image [ISO 19795-2, 2007]. 

The tests in the iris PoC have been instrumented to collect sufficient data to conduct both a specific 

scenario evaluation and follow on technology evaluation. 

3.2 Iris Authentication Concepts 

Prior to presenting the authentication accuracy results, terms used in this document are defined 

below. 

1. Session: Visit. This PoC uses only one session. 

2. Sample: User’s biometric measures as output by the data capture subsystem. For e.g., face 

image, fingerprint image and iris image are samples. 

3. Presentation: Submission of a single biometric sample on the part of a user. It may also be 

referred to as impression. On single eye camera, each presentation may generate one image. 

One presentation of two eye iris camera generates two images. 

4. Attempt: Submission of one (or a sequence of) biometric 

samples to the system after it has passed quality check. 

NOTE: An attempt results in a template and can be used 

to generate a matching score. There is a difference 

between the definition of attempt by ISO and this PoC 

attempt. This PoC attempt is after quality check. A 

failure to acquire due to missing fingers/iris or inability to 

© UIDAI, 2009-2012 Page 12 of 36 

Figure 3 Residents waiting for their turn


even register image on scanner does not result in an attempt. 

5. False reject rate (FRR): Proportion of verification transactions with truthful claims of identity that 

are incorrectly denied. All data collected from the field included an operator-set flag for the 

expected response (the Ground Truth). Based on this, all ‘true’ authentication transactions were 

rerun in the lab and the matching scores recorded. This allowed capture of the false reject rate 

for a particular threshold. 

6. False accept rate (FAR): Proportion of verification transactions with wrongful claims of identity 

that are incorrectly confirmed. Expected false requests were created by pairing biometric data 

collected from the field with an Aadhaar number other than the original Aadhaar holder 

(imposter requests). A large number of these requests were rerun and matching scores 

recorded. This allowed capture of the false accept rate for several thresholds. As many false 

requests as necessary were created to provide statistically significant results. 

7. Failure-to-capture (FTC) includes: 

� Attempts where the biometric characteristic cannot be captured; 

� Attempts in which the extracted features do not meet the quality control thresholds; 

� Attempts for which the segmentation or feature extraction fail; and 

� Attempts where the raw sample was not acquired or did not meet quality thresholds are 

not processed by the matching algorithm, and do not generate matching scores. 

8. Transaction: Entire on-line process of capturing biometric sample, packaging it for on-line 

submission, communicating to the backend server (CIDR), backend server matching and 

receiving of the response. It began when the resident provided his/her Aadhaar number and 

ended when the response was received. 

9. True Accept Rate (TAR): TAR = 1 – FRR. 

3.3 Test Methodology 

Since collection of biometric data from the residents is an expensive process, the multi-modal 

authentication PoC covered both scenario and technology testing in one session. The complete 

process that included several more steps took about 15 minutes per resident and consisted of 

1. One Best Finger Detection procedure (UIDAI, 2011) (UIDAI, 2012) 

2. Two online fingerprint authentications (scenario test) 

3. One face picture 

4. One online iris authentication on one of the iris authentication cameras (scenario test) 

5. 2 “best” images on each of 8 iris authentication cameras (technology test) 

6. 2 “best” images on each of 6 iris enrolment camera (technology test) 

This report covers results of iris authentication only (steps 4 and 5). 

The session setup consisted of two parts. The resident went through both two parts. Part 1 covered 

scenario testing and part 2 covered technology testing. 

Part 1: Scenario Testing. The scenario testing utilized Aadhaar’s 

production environment. It was performed on-line and in real 

time. The authentication frontend was connected with the CIDR 

through mobile network. The resident was authenticated in real 

time against the enrolment data. 

Figure 4 Resident at POC site 

© UIDAI, 2009-2012 Page 13 of 36


Part 2: Technology Testing. The test collected a number of presentations (images) of iris for use in 

off-line analysis. 

Iris authentication devices come in a variety of forms and designs. Available models were tested to 

determine readiness of iris authentication including suitability, usability, interoperability and 

performance of the camera. 

In scenario testing, it is important to measure process parameters such as end to end transaction 

time and number of attempts. Accuracy results were obtained from technology test. Technology 

test also provided device level performance. Technology tests were done using offline data 

collection. Raw uncompressed images were captured. Images were compressed using JPEG 2000 

lossy compression during off line analysis. 

3.3.1 Iris Capture attempts under scenario testing 

Iris images were captured using maximum of 3 attempts of up to 3 presentations. All images were 

captured automatically by the device, without operator forcing the capture. 

1. Each successful presentation resulted in image capture. Unsuccessful presentation, where 

the camera was unable to capture image was still counted as presentation. 

2. Each captured presentation was checked for quality using SDK’s quality algorithm. If the 

quality was below acceptable threshold, the image was captured again (i.e., second 

presentation). The final image was either the first image meeting quality threshold or best 

image among the three presentations, whichever comes first. Final image was sent to the 

back end for authentication using the chosen mode of communication. 

3. If the authentication failed, the resident was asked to undergo step 1 & 2 again up to two 

additional attempts. 

4. FTC condition would occur if no image was captured after three attempts as mentioned in 

step 1. 

Single eye camera: Each presentation was defined as image of one eye. Second attempt used 

different eye from the first. In other words, the recommended multiple attempt sequence was right 

eye, left eye, right eye. The switching of eyes effectively provides “best finger” strategy for two 

eyes. 

Dual eye camera: Each presentation was defined as one image of each eye (two iris images). While 

other aspect of process was same (3 attempts of 3 presentations each), the maximum number of 

images captured would be twice that of single eye camera. Dual eye camera is effectively equivalent 

to using two fingers for authentication in previous studies. 

3.3.2 Iris Capture attempts under technology testing 

Maximum of two images of each eye were captured using up to 5 attempts. All images were 

captured automatically by the device, without operator forcing the capture. 

� Each successful presentation would result in image capture. Unsuccessful presentation, 

where the camera was unable to capture image, was skipped. 

© UIDAI, 2009-2012 Page 14 of 36

� Each captured presentation was checked for quality using SDK’s 

quality algorithm. If the quality is below acceptable threshold, 

the image was captured again. The process continued until two 

quality images are captured or maximum of five attempts, 

whichever comes first. If the process did not generate two 

quality images, two best quality images of each eye were 

selected from available images. 

� In case of single eye camera, above steps were repeated for 

each eye. In case of dual eye camera, even when two eyes are 

captured, the above process was repeated until two images of 

each eye is collected. In other words, regardless of camera 

type, two “best” quality images of each eye are collected. 


� FTC condition was recorded if no image could be captured in five attempts. 

Figure 4 Resident at POC site 

3.3.3 Iris Stations for scenario testing 

The resident was authenticated at only one of the iris stations. In other words, with eight iris 

camera stations, each station received approximately 1/8 th of the residents. The distribution of 

residents to the iris station was not controlled. 

1. Iris authentication during scenario testing. 

a. All the cameras were handled by the operator during the test. 

b. Attempts: Section 3.3.1 details process to be used for multiple attempts. 

c. Quality check: UIDAI’s quality check SDK was used whenever the device vendor did not 

supply it. 

d. Image format: Vendor supplied software to generate ISO image (VGA) was used. Images 

were compressed using JPEG 2000 lossy compression. 

e. Image selection: In case of one eye camera, best image was the first image that passed 

quality check or the best image of the attempt. In case of two eye camera the images 

within an attempt would be buffered and best image of each eye was sent. 

2. The operator recorded eye position (left or right) using provided UI. 

3. Matching: The resident is authenticated when match occurs in any attempt. No further 

biometric capture is done at the station for the resident. 

4. If a resident is unable to authenticate at the station, the resident is not excluded from going to 

technology testing section. 

3.3.4 Iris Stations for technology testing 

1. The resident authenticated using iris through the following steps. 

a. All cameras were handled by the operator. 

b. Auto capture function captured iris image(s). If one-eye camera was used, one image 

was captured. If two-eye camera was used, two images were captured. 

c. Eye: The default first attempt was right eye unless operator determined that the 

resident’s right eye was not ideal. This was not relevant for two eye cameras. 

d. The operator was required to note the eye position in the software for single eye 

camera using the interface provided. 

e. Attempts: See Section 3.3.2. 

f. Quality check- SDK quality check used in enrolment was used. 

© UIDAI, 2009-2012 Page 15 of 36


g. On-line authentication. It was not necessary to perform on-line authentication. All iris 

stations were off-line. 

h. Raw images were stored. 

Figure 5 Screen shot of Client UI 

2. If a resident was unable to provide iris image at one station, the resident was not excluded from 

going to subsequent stations. For such residents, FTC (Failure to capture) event is recorded. 

3.3.5 PoC Client Application and Data Collection 

As part of the PoC client application, to measure different variables and compare/fine-tune 

performance of various SDKs, detailed logs were captured for further analysis. At the UIDAI 

Technology Centre, all logs were analyzed and authentication requests were replayed on multiple 

SDKs to understand the impact of various biometric matching algorithmic changes and other 

backend interventions that could be used to improve the authentication accuracy. POC application 

prominently displayed quality parameters along with captured iris. 

3.3.6 Vendor Participation 

In this study, Iris camera vendors were invited to participate based on an EOI (EOI No. K- 

1102/16/2012-UIDAI dated 7 th March, 2012). Following an initial evaluation, vendors selected for 

the study, as summarized below: 

Measure Number 

EOI responses received 11 

Number of models ready with H/W and S/W by due date 8 

Number of models participated in PoC 8 

Number of OEM participated 6 

Number of devices which were made ready post POC and 

hence could not participate in PoC 

1 

Table 2: Vendor Participation 

© UIDAI, 2009-2012 Page 16 of 36


3.3.7 Field Setup 

Authentication PoC centers were designed to resemble expected ground reality. No attempt was 

made to create an ideal situation. Some key considerations for setting up and managing the 

authentication stations were as follows: 

� UIDAI procured all the components and standardized them across all authentication PoC 

stations – such as laptops, network connectivity, power backup, etc. EOI participants 

provided cameras and capture software. UIDAI performed final software integration with 

PoC UI. 

� Commercially available telecom data card (GPRS) used for network connection in order to 

send authentication requests to CIDR. 

� Regular electrical supply available at the PoC location, backed up with UPS/ generators was 

used for the PoC exercise. Disruptions in the main power supply were noticed during the 

course of the PoC. 

� Sensors were deployed across various stations to provide equal opportunity to all 

participating device technologies / OEMs, constrained by USB port availability. 

� The key consideration was to get each resident to authenticate using all the authentication 

devices. 

� For resident mobilization, residents were requested to participate in 

the Proof of Concept studies through announcements by local 

administration. Announcer was made to move into surrounding 

villages and to draw the resident population for the studies. 

During the field operation, some of the deployed devices intermittently 

failed causing system hang-ups or freeze. Considering the larger PoC 

objectives & crowd management issues, the devices had to be disconnected 

temporarily. As a result not every resident went through every device. 

3.4 Resident and operator communication and training 

Detailed communication material was developed to educate the operators 

Figure 6: Announcer 

and residents to conduct the process as well do’s and don’ts. Banners and 

Standees were placed in and around the PoC site. Laminated sheets containing these instructions 

were handed over to residents as they entered the PoC site in order for them to acquaint 

themselves with the process to follow during the capture. Operators were also trained using the 

same instructions. 

Figure 7: IEC material used during POC 

© UIDAI, 2009-2012 Page 17 of 36

3.5 Data Quality & Data Analysis 

Data analysis consisted of the following steps: 

� Analyzing the logs from field 


� Replaying the images captured from the field against the authentication server 

� Visually reviewing images to eliminate errors. Several types of errors were identified and 

rectified 

o Labeling errors. Wrong demographical information, wrong label of right or left eye 

o Enrollment errors. Residents whose authentication images could not be matched 

because the corresponding enrolment data consisted of extremely low quality 

images, etc. These residents (8 residents) were identified and not considered in the 

accuracy analysis. 

© UIDAI, 2009-2012 Page 18 of 36

4 Feasibility of using iris for authentication 

This section summarizes the following areas from the iris study: 

� Demographics of the study participants (scenario test) 


� Coverage - Resident population who were able to have their iris captured for authentication 

(technology test) 

� Accuracy - Matching accuracy using various matchers (technology test) 

� System readiness - Capture time, Network time related characteristics (scenario test) 

� Devices readiness and related findings (technology test) 

In scenario testing, 17,990 online authentication transactions were performed by 5,747 residents on 

available GPRS mobile network. The 5,833 residents participated in the iris technology test resulting 

in 40,148 capture sessions on 8 authentication cameras. In all, 215,342 iris images were capture 

during this process. 

4.1 Demographic Profile 

The demographic profile of participants in the iris study was compared with the profile of residents 

enrolled in Aadhaar – both nationally as well as within Mysore district. The comparison was done on 

the basis of participant age bands, as well as gender. 

It was found that children in 5-15 years range were under represented in the sample compared to 

overall Aadhaar enrolment statistics, while senior residents (66+ years) were over represented. 

Similarly, females were over represented in the study. 

The following charts show the age and gender distribution of all Aadhaar holders nationwide, in 

Mysore district and participants in the iris PoC. 

Poc 

Mysore 

India 

9 

18 

18 

83 

0% 20% 40% 60% 80% 100% 

77 

78 

Figure 8: Age distribution of participants in study compared to Aadhaar enrolment 

© UIDAI, 2009-2012 Page 19 of 36 

8 

5 

4 

6-15 years 

16-65 years 

66 and above

Poc 

Mysore 

India 


Figure 9: Gender distribution of participants in study compared to Aadhaar enrolment 

4.2 Coverage 

Coverage is primarily measured by failure to capture (FTC) rate. Since devices with different 

characteristics and quality were utilized, FTC rate over all devices does not accurately measure 

maturity of iris technology. A new metric that measures inherent technology limitation in capturing 

iris images in Indian context was also introduced. This measured the number of residents who could 

not use any device and could not use several devices. 

4.2.1 Resident coverage across multiple devices 

Two conclusions can be drawn from the statistics: 

45 

50 

52 

0% 20% 40% 60% 80% 100% 

Table 3: Failure to Capture 

� The coverage achieved by iris authentication was high, demonstrating the potential of iris to be 

an inclusive modality. There is no inherent technical or physiological limitation to using iris for 


� The observed FTC rate of 0.33% (based on failure across 4 or more cameras) indicates that it 

should be possible for most of the residents to conveniently authenticate using iris. 

Since the resident’s iris image capture was attempted on different cameras in one session, it was 

possible to manually review cases where image capture failed on one camera but succeeded on 

another. While the FTC rate was very small, the most frequent cause for those failed was due to eye 

conditions such as deformed pupil/iris (mostly result of surgeries), antiquated cataract surgery or 

other types of eye abnormalities. Majority of the cases were from non-circular pupil shape caused 

due to cataract operations using Intra Capsular Cataract Extraction (ICCE) cryo-surgery technique 

used prior to 1990. 

© UIDAI, 2009-2012 Page 20 of 36 

55 

50 

48 

Male 

Female 

Resident coverage across 8 cameras Number of residents % failure 

Total no. of Residents who participated 5833 

Neither eye could be captured in all cameras 1 0.017% 

Only left eye was captured (right eye not captured) 3 0.051% 

Only right eye was captured (left eye not captured) 9 0.154% 

Failed iris capture (either eye) on 4 or more cameras 18 0.308% 

Figure 10: ICCE Cataract surgery

Other causes observed were from: 

Figure 11: Occlusion 


� Residents with squint eyes often suffered from FTC especially on dual eye camera. Single eye 

cameras are able to capture one eye with less difficulty. 

� Some residents could not open their eyes sufficiently leading to FTC or false rejects (Figure 11). 

� Some residents suffered from involuntary movement of the eye-ball (Nystagmus) and hence had 

an unstable iris resulting in FTC. 

In the analysis, failure due to segmentation, if any, was included in FTC. Failure in template 

generation, if any, was included in false reject statistics. 

4.2.2 Failure to Capture (FTC) by Camera 

FTC rates are computed by device and can provide another perspective on the capture failure. 

Single Eye Camera FTC Dual Eye Camera FTC 

A 0.12% E 0.61% 

B 2.01% F 2.13% 

C 0.30% G 9.84% 

D 0.10% H 0.31% 

Table 4: Camera-wise FTC 

The failure to capture (FTC) of individual iris cameras varies widely by camera as shown in Table 4. 

When compared to median performance of peer devices, camera “G” performed poorly and was 

judged to be an outlier. 

Improvements in capture software and ergonomics can enhance camera performance. These issues 

are discussed in detailed as part of device recommendations under Section 4.5 and in Annexure 

4.3 Accuracy results from technology test 

The most common measure of accuracy is True Accept Rate (TAR) and False Accept Rate (FAR). Just 

like coverage it is instructive to use additional measure, called resident accuracy, to understand 

behavior of iris technology across cameras. 

All biometric matchers require FAR threshold as input to calculate matching score which is used to 

determine FRR. Accuracy calculations shown below are done at FAR of 1e -6 or 1 in 1,000,000. 

4.3.1 Resident Accuracy 

Of the 5833 residents who participated, 5793 residents were able to successfully authenticate using 

at least one authentication camera using either their left or right eye. Of remaining residents, 8 

could not be authenticated due to incorrect enrolments (i.e. issues with enrollment gallery), while 13 

residents (0.22%) could not be successfully authenticated using either the left or right iris on all 

© UIDAI, 2009-2012 Page 21 of 36


cameras. It is indicative of issues inherent to the image and not capture or matching algorithm. 19 

residents were FTC in at least four cameras. 

Accuracy centric statistics 

Table 5: Resident wise Accuracy 

4.3.2 Device Accuracy 

The accuracy of individual iris cameras varied widely. The FRR achieved using up to 2 attempts of 

both left and right iris (FAR of 1e-6) is shown in Table 6 for single eye and dual eye camera. 

Compared to median performance of peer groups, two cameras – C and G – were considered to be 

outlier. Others are referred to as “good” cameras. Further analysis on the cameras can be found in 

the Annexure. Remaining six cameras are used for all further analysis unless noted otherwise. 

Single Eye Camera FRR Dual Eye Camera FRR 

A 0.52% E 0.21% 

B 0.43% F 0.31% 

C 0.91% G 0.85% 

D 0.41% H 0.28% 

Table 6: Camera wise Reject Rates (2 irises with 2 attempts) 

4.3.3 Impact of using two irises and multiple attempts 

Single eye camera captures one iris at time. The 3 “good” single eye cameras achieved a true accept 

rate (TAR) 96.21% in one attempt. This means that 96.21% residents can be authenticated using a 

single (left) iris capture. When the other (right) iris is added in matching the percentage of resident 

successfully authenticating improves to 99.23%, again in single attempt. Capturing both the left and 

right iris a second time (second attempt) improves the TAR to 99.54%. 

There is a significant improvement in the TAR rate, between a single (left) iris capture and both iris 

capture. 

Single Eye Camera Single Iris TAR 

Number of 

residents 

Both Iris 

1st Attempt TAR 

Both Iris 

2 Attempts TAR 

All Cameras (4) 93.77% 98.99% 99.43% 

Good Cameras (3) 96.21% 99.23% 99.54% 

Table 7: Single Eye Camera - Good and all single eye camera TAR 

% of residents 

Resident successfully with either their left or right eye (on 

at least one camera) 

5793 99.45% 

Resident did NOT authenticate successfully on any eye(right 

or left) 

13 0.22% 

Resident did NOT authenticate due to enrolment issues 8 0.14% 

FTC (image capture failed on four or more cameras) 19 0.33% 

Dual eye camera captures both irises at the same time. The “3” good dual iris cameras achieved a 

TAR of 99.62% in the first attempt. A second attempt of the dual iris capture improved the TAR to 

99.73%. 

© UIDAI, 2009-2012 Page 22 of 36


Dual Eye Camera Both Iris 1st Attempt TAR Both Iris 2 Attempts TAR 

All Cameras (4) 99.46% 99.62% 

Good Cameras (3) 99.62% 99.73% 

Table 8: Dual eye camera - Good and all Dual eye camera TAR 

The data shows that high accuracy can be obtained using both single eye and dual eye cameras. 

Performing a second attempt of capturing the iris slightly improves the TAR for both single and dual 

iris camera. It is estimated that third attempt may not further improve accuracy. Hence it is safe to 

specify maximum attempts at two in standard operating procedure. 

4.3.4 Matcher Performance 

The images captured were compared using two different backend 

matchers (SDK). The graph below illustrates the session wise (up to 2 

attempts of both irises) FRR achieved using the two SDK. Matcher 2 

had an FRR of 0.43% while matcher 1 had an FRR of 1.11%. Matcher 2 

has been used for all other comparisons in this report. 

When falsely rejected images were manually reviewed, it was noticed 

that Matcher 2 was able to match off axis (gaze), dilated, out of focus 

and motion blur images better than Matcher 1. This was a significant 

contributing factor in superior performance of Matcher 2. 

4.3.5 Iris DET Curve 

Figure 12 FRR by matchers 

Iris authentication has a flat DET curve, i.e., there is only a slight 

degradation in FRR for significant reduction in FAR. As shown in the DET curve in Figure 13, the 

single eye camera FRR (2 iris, 2 attempts) increases from 0.31% for an FAR of 1e-3 to an FRR of 

0.46% for an FAR of 1e-6. 

FRR 

1.00% 

0.80% 

0.60% 

0.40% 

0.20% 

0.00% 

0.46% 

Iris DET (2 Iris, 2 Attempts) 

Single IRIS Camera Dual IRIS Camera 

0.34% 0.33% 0.31% 

0.27% 0.23% 0.22% 0.21% 

1.00E-06 1.00E-05 1.00E-04 1.00E-03 

FAR 

Figure 13 DET Curve - Single and Dual Iris Authentication 

As shown in the DET curve in Figure 13 the dual iris camera FRR (2 iris, 2 attempts) increases from 

0.21% for an FAR of 1e-3 to an FRR of 0.27% for an FAR of 1e-6. 

Hence, iris can be a particularly attractive option for high security applications such as financial 

transactions or secures access control. Iris authentication can operate at TAR > 99% with FAR = 1e-6. 

© UIDAI, 2009-2012 Page 23 of 36


4.4 System findings 

Besides accuracy and coverage, the third most important factor for feasibility of iris based 

authentication is system level factors. These factors consist of 

� Response time 

� Image size used in transmission. 

� Matcher speed 

The response is measured with two metrics: Resident authentication time and round trip time. 

Resident authentication time includes time to select the device, enter the Aadhaar number, guiding 

the resident for correct capture procedure, completing the capture of both eyes, submitting the 

transaction and receiving response. Round trip time measures the last two parameters of the 

authentication time during which the resident is waiting for system to respond. 

4.4.1 Resident Authentication Time 

Figure 14 is a histogram of authentication time 

separated by single and dual eye cameras. 

Median time in both cases is below one minute. 

In case of single eye devices, one iris is captured 

at a time whereas in case of dual eye device, both 

irises are captured at a time. Therefore, median 

time of dual eye camera is lower than that of 

single eye. 

This time distribution also includes the time 

taken for repeated impressions due to failed 

quality or failure to capture during the first 

attempt. 

4.4.2 Round trip time 

The histogram of round trip time under these 

conditions is shown in Figure 15. Median is below 

ten seconds. Over 62% of transactions returned in 

less than 10 seconds, while only 7.8% of 

transactions took more than 15 seconds. 

The network performance can be further improved 

by smaller image sizes as discussed in the next 

section. 

© UIDAI, 2009-2012 Page 24 of 36 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

0% 

0.4% 

24.0% 

65.4% 

60.4% 

31.0% 

Single Eye device 

Dual Eye Device 

13.6% 

3.3% 

2.0% 

< 30 sec


The diagram below shows compression used in this PoC and the roadmap to get the device vendors 

to support kind 7 images which will reduce the iris image to 2KB. Refer [ISO 19794-6-2011] for 

further definitions of image types cited above. 

Figure 16: Image Formats 

4.4.4 Matcher Speed 

Previous PoC demonstrated that current setup is capable of supporting sustained fingerprint 

authentication speed of one million per hour. It has been demonstrated repeatedly in the literature 

that iris matcher is more efficient than fingerprint matcher. Therefore no independent tests were 

performed using iris matcher. Based on published data, it is safe to state that Aadhaar is capable of 

sustaining iris authentication rate of over one million per hour. Further tests could verify this 

assertion. 

4.5 Devices Findings 

During the PoC, four cameras were single eye cameras and four cameras were dual eye cameras. 

The cameras varied in form and function. Satisfactory performance of six cameras supplied by 

different OEMs indicates that cameras will be competitively available in different form and in desired 

quantities. 

All devices provided documentation on mode of capture and were easy to learn and operate from 

operator and resident point of view. Similarly 8 of 11 device vendors provided software in 

compliance to API specifications published for the purpose of POC. One vendor achieved 

compliance after POC process was completed and could not therefore be tested. 

All the devices used during the test, were supplied with rugged casing. No breakages and 

malfunctioning due to packaging were reported during the POC. 

© UIDAI, 2009-2012 Page 25 of 36


Devices supported auto capture feature. This enabled devices to automatically capture good quality 

iris pictures when operator held the device at the specified capture distance from the resident iris. 

Single eye devices captured single iris at a time. Dual eye devices captured both irises at the same 

time. There was one device which was capable of dual iris capture, but was configured to capture 

both irises, in a sequence, one iris at a time. When presented with residents who had one eye in 

good condition, single eye devices fared better than dual eye camera which looked for two good 

irises. Dual iris cameras could improve the capture algorithms in order to overcome this issue. 

Figure 17: Sample images of residents providing samples during POC 

From usage model point of view, capture distance and capture volume of iris devices used varied 

widely. Capture distance (distance between edge of device and eye) of less than10 mm to over 

300mm were tested during the POC. All but one device were expected to be handled by the 

operator and last device was mounted using tripod. As the capture volume supported by the device 

plays important role in ease of capture, devices with larger capture distance and small capture 

volume required additional training for both operators and residents. 

Similarly, actionable feedback and capture aids supported by the devices played key role in ensuring 

high quality and easier iris captures. Most devices were providing actionable feedback to the 

operator and capture was supported through various aids. Devices that provided no feedback and 

capture aids took more number of presentations to achieve high quality of iris capture. 

VGA images provided by devices were compressed and analyzed during the analysis. Iris images 

were compressed to 15KB for the analysis. However, devices supporting cropped/masked (Kind 7) 

images would help reduce the size of the iris further to order of 2KB. This reduction will help during 

iris online transactions. It has been shown by NIST report [Grother, P, 2009] that such reduction 

does not have accuracy related implications. 

© UIDAI, 2009-2012 Page 26 of 36


The annexure provides further information related to device readiness and areas of further 

improvements. 

4.6 Other findings 

4.6.1 Impact of Resident Age 

The impact of age on authentication accuracy was studied. The computed DET curves compare the 

accuracy of matching for people in the age group 5-15, 15-60 and 60+ years. 

FRR 

1.20% 

1.00% 

0.80% 

0.60% 

0.40% 

0.20% 

0.00% 

Age wise DET Curves (2 Iris, 2 Attempts) 

1.06% 

0.29% 

0.19% 

0.87% 

0.84% 

0.22% 0.21% 0.20% 

0.14% 0.14% 0.14% 

Figure 18: Age wise DET chart 

0.81% 

1.00E-06 1.00E-05 1.00E-04 1.00E-03 

FAR 

For all age categories, overall accuracy continues to be higher than 98.94% at FAR of 1e-6. Among, 

those children have performed best (0.19% reject rate, statistically not significant), followed by 

adults (0.29%) and seniors (1.06%). Some of the seniors could not be authenticated due to special 

eye conditions. 

© UIDAI, 2009-2012 Page 27 of 36 

< 15 

15-60 

>60

5 Observations & Findings 


In conclusion, a set of observations and findings derived from the analysis of the PoC data is 

presented below. 

5.1 Coverage 

It is possible to achieve over 99.5% population coverage for on-line iris authentication. Most of 

those whose iris images could not be captured on four or more cameras suffered from special eye 

condition, mostly due to older generation of cataract or eye surgery that resulted in highly irregular 

shaped pupil or iris. With current devices and matcher algorithm, they cannot avail themselves to 

iris authentication. Alternate method such as fingerprint or OTP will be required. They could be 

easily identified using enrollment data or during first attempt to authenticate. 

Fingerprints of these individuals with special eye conditions were reviewed. It was found through 

best finger detection (BFD) and authentication that their fingerprints were of sufficient quality to 

enable fingerprint based biometric authentication. 

5.2 Accuracy 

It is possible to attain true accept rate of over 99% on both single and dual eye cameras with 

stringent FAR of 1e-6. 

Authentication Mode Single eye camera Dual eye camera 

One eye authentication 96.21% Not Applicable 

Two eye authentication 99.54% 99.73% 

Table 9: True Accept Rate at FAR = 1e-6. 

Operating at false accept rate of one in million, Iris technology also lends itself for highly secure 

authentication against imposter attacks. Table 9 shows how high accuracy authentication may be 

achieved using various strategies. 

Use of second iris for authentication improves accuracy by 3% over single iris. However, second 

attempt of the same iris only marginally improves accuracy. Due to diminishing return, it is unlikely 

that third attempt would increase accuracy any further. 

For all age categories, overall accuracy continues to be higher than 98.94%. Among those children 

have performed best, followed by adults. Some of the seniors could not be authenticated due to 

special eye conditions. This indicates the need to tune the capture and matcher algorithms 

specifically for seniors to accommodate for special eye conditions. 

A smaller percentage of people (0.14%, not statistically valid) could not be authenticated because of 

enrollment data quality issues. They would be able to use iris authentication once their biometric 

data is updated. 

Anecdotally, blindness by itself was not perceived to be an issue and blind people without special 

eye conditions authenticated successfully. 

5.3 Device Readiness 

There is a variation in authentication accuracy cameras based on capture algorithms, ergonomics 

and related factors. In the study, it was observed that while most cameras performed well, there 

© UIDAI, 2009-2012 Page 28 of 36


were outliers with high FTC and FRR rates. Operator’s difficulty in using them correlated with their 

poor FTC and FRR rates. For example, one camera had FTC rate of about 10% while most others 

were operating below 1%. It is critical that only good cameras are selected for deployment in the 

field through a rigorous testing and certification process. 

Single eye camera was judged better with people with squint eye. Residents found dual eye camera 

easier to use resulting in slightly faster capture time. Dual eye camera that captures one eye at a 

time seems to offer best of both the worlds. 

Devices providing capture aids and actionable feedback help operator with faster and better quality 

capture. The devices which provided effective capture aids and feedback to operator regarding 

capture process and image quality was seen to achieve very high degree of accuracy. 

It is important to emphasize that the quality of image plays an important role in ensuring 

authentication accuracy. Devices have incorporated quality measurements within their capture 

algorithms to enable auto capture. In addition to the existing parameters, to further improve the 

quality of capture and minimize number of attempts, algorithms could check for gaze correctness, 

rotated device, upside down eyes, occlusion, focus, motion blur and special eye conditions. 

Vendor supplied software provided iris image in KIND 1 and KIND 2 formats. Resulting compressed 

image was 15 KB in size. KIND 3 and KIND 7 could bring this size down to 2 KB which would be 

beneficial in reducing network latency and improving network reliability. 

5.4 System Readiness 

The median time to perform end-to-end online authentication is less than one minute out of which 

about a quarter is for round trip transmission (& back end processing which is sub second). From 

resident’s view, the wait time after capture has occurred is less than 15 seconds which was judged 

acceptable. 

Faster network or smaller image size will reduce resident wait time. Device improvement 

recommendation mentioned in this report will also improve transaction time. 

5.5 Findings 

1. One eye vs. two eyes: Two irises authentication provides significant improvement in accuracy 

and coverage over one iris. In case of single eye camera, it is recommended to authenticate first 

with one eye, and only in case of failure, the second eye be used for authentication. 

2. Attempts: Two attempts are recommended. While the second attempt only marginally 

improved accuracy, it compensates for other forms of human error should they occur. 

3. Device Certification: Stringent authentication device testing and certification is recommended to 

ensure high quality authentication devices are deployed for Aadhaar authentication. 

4. Capture aids: Device vendors should provide better capture aid for operator and residents. The 

study categories three types of aid: actionable feedback, visual aid and “appropriate light source 

to improve quality of image and finally measures to block ambient light reflections. These 

improvements detailed in Annexure will reduce capture time, improve accuracy and offer better 

user experience. 

© UIDAI, 2009-2012 Page 29 of 36


5. Capture quality: It is important to emphasize that quality of image plays important role to ensure 

authentication accuracy. Devices should make attempt to incorporate specific areas of quality 

explained in the document along with what is being already being done within the devices to 

ensure quality. 

6. KIND 7 image formats: Device vendors need to implement KIND 7 format which is more efficient 

than the current standard used. 

7. Device Enhancement: The study has clearly identified a small number of residents (< 0.3%) with 

special eye conditions. Vendors would be provided with the images (suitably anonymized) and 

UIDAI would work with them to improve device capture and iris matching algorithms to handle 

such cases. 

8. Matcher SDKs: Since there was a variance in the performance of two matcher SDKs tested, the 

matcher SDK vendors need to enhance their algorithms to provide higher tolerance to problems 

arising out of incorrect gaze, dilation, focus, motion blur etc. 

9. Matcher Setting: Since FRR does not materially change when FAR is changed from 1e-4 to 1e-6, 

iris lends itself well to be operated at much lower FAR thus providing robust security against 

imposter attacks. 

5.6 Way Forward 

UIDAI would be working with the biometric ecosystem to implement the findings mentioned in this 

document and take up further field studies to fine-tune iris authentication technology. These studies 

would also lead to formulation of iris device specifications which will be used for certification by 

STQC. 

© UIDAI, 2009-2012 Page 30 of 36

6 References 


[Grother, P, 2009]: IREX Summary. By P. Grother, E. Tabassi, G. W. Quinn, W. Salamon: NIST. 

[ISO 19795-2, 2007]: Biometric performance testing and reporting—Testing methodologies for technology and 

scenario evaluation. By International Standards Organization. 

[ISO 19794-6, 2011]: Information technology -- Biometric data interchange formats -- Part 6: Iris image data. 

By International Standards Organization. 

[UIDAI, FP, 2012]: Role of Biometric Technology in Aadhaar Authentiation - Detailed Report 

http://uidai.gov.in/images/role_of_biometric_technology_in_aadhaar_authentication_0 

20412.pdf 

© UIDAI, 2009-2012 Page 31 of 36

7 Appendix – Camera related findings 


This section is concerned with devices used in the field exercise. The section goes into detailed 

findings and observations including future areas of improvements to improve capture efficiency and 

ergonomic improvements. 

7.1 Capture distance and Capture Volumes 

During the proof of concept, four cameras were single eye cameras and four cameras were dual eye 

cameras. Most of the single cameras had capture distance of less than 3cm. One dual iris camera 

had a capture distance of about 33 centimeters. Camera distance and volume of cameras used in 

the POC is listed below. 

Camera Single or Dual Iris Capture Distance Capture Volume ( WxHxD) 

Camera 1 Dual 22.5 cm to 25.0 cm 15 cm x 11 cm x 4 cm depth. 

Camera 2 Dual 0 to 10 mm from forehead 42 x 24 x 30 mm for each eye 

Camera 3 Single 120mm 39x25x20mm 

Camera 4 Dual 315 to 345 mm 60 x 45 x 30 mm for each eye 

Camera 5 Single 

50mm(from sensor)- 0-10mm 

from the eye guard 

33x24.5x8mm(D) 

Camera 6 Dual >58mm 20x15x12mm 

Camera 7 Single >58mm 20X15X12mm 

Camera 8 Single 

0 to 10 mm from the eye 

guard 

32 x 24 x 10 mm 

Table 10 Camera Capture distance and capture volume information 

Note: Cameras have been listed above in alphabetical order and are not to be correlated with other 

camera listing tables. 

7.2 Image Capture and Output 

All participating camera vendors were requested IRIS segmentation SDK which produces image in 

format Type 1, 2, 3 and 7 (also known as Uncropped, VGA, Cropped and Cropped & Masked 

respectively) compliant to ISO 19794-6 standard. It was observed the camera vendors support for 

Uncropped and VGA formats (Kind1 and 2 respectively as per ISO 19794-6 standard) (ISO, 2011) was 

complete and support for Kind3 (cropped) and kind7 (masked) was not complete. 

Support for Kind7 (Cropped & Masked) is key for iris authentication to be viable in the field due to 

size of the image. Crop/masked images allow for close to 2kb images which tends to be relatively 

easier on transmission during online authentication. 

All devices supported auto capture. Forced capture was not implemented across devices uniformly 

and hence was not used. 

Auto capture algorithms on few cameras failed to capture in instances where special eye conditions 

were presented (see section 8.4). It is very important for the auto capture algorithms be tuned to 

capture resident iris with special eye conditions in order to improve the coverage of iris 


© UIDAI, 2009-2012 Page 32 of 36


Cameras that seek to block ambient light provide visible light source inside the camera. Visible light 

source serves two purposes. It prevents dilation of pupil that would otherwise occur in a dark 

environment. It helps resident to maintain proper gaze. Residents complained about some light 

sources to be too bright to cause stress. When light source was not present, the residents had to be 

instructed to look straight. Providing resident focus points such as a mirror so resident can see 

his/her own eyes inside will also help retain gaze. 

Two single cameras supported eye flaps to help position the device close to eyes softly. These flaps 

could rotate 180 degrees to capture either of the eyes. Although this feature was innovative and 

helped operator and resident to capture easily, room for error due to non-rotation of flaps for each 

capture may cause bad quality images as flaps tend to obstruct movement of device close to eyes 

and nose, when not rotated. 

All the devices were connected to the PC through USB interface. Few cameras required independent 

power source while most drew power through USB. This makes certain cameras useful for mobile 

field requirements with handheld usage while others which require continuous power suitable for 

desk usage with continuous power availability. 

7.3 Capture aids 

7 Devices out of 8 implemented one or many capture aids during the capture process to provide 

support and actionable feedback to operator. Actionable Feedback provided to operator falls in one 

or more of the following categories. 

1. LCD display for operator to visually see the camera output. By moving device closer/farther 

the operator was able to get improve the focus and quality. 

2. Using small lights behind the camera to indicate capture start and end process. This helped 

operator to position the device at the right distance for the capture process to begin and 

also know when the process completed. 

3. Changing color of the light to indicate when the resident is too far or too close or at the right 

distance. For example, light color would change to blue when the resident moved too close 

or and turned to red when the resident moved too far and turn green when the resident is in 

the right range. 

4. Devices using visible light source also provided feedback in that when the source was turned 

on, by watching the eye socket being lit, operator could infer that capture process having 

started and similarly when it was turned off, infer that capture process was completed. 

However, if the room ambient light was high, then operator might not be able to notice the 

light source from the camera. Excess light source was seen to result in constriction (reduced 

pupil) affecting the ability of the matchers to segment iris. 

5. Provide operator feedback related to capture process on the PC. Messages such as resident 

being far, close and at the right distance etc. were provided to operator. However it 

required that the operator watch both PC and camera at the same time. Operator feedback 

should be incorporated in the camera. 

6. Alignment aids in terms of view finder for operator to align the camera to the resident eyes. 

This helped operator to align the device to the eye level. 

It was observed that providing capture aids and actionable feedback to the operator 

improves the capture process and enhances the quality of capture. Actionable feedback 

© UIDAI, 2009-2012 Page 33 of 36


becomes essential when the capture distance is high and capture volume is not big enough 

for quick and high quality capture. Lack of capture aids/effective operator feedback was 

seen to make the capture process harder. 

There is scope for providing further capture aids and more actionable feedback for the 

operator. These areas include 

1. Feedback related to quality of the image - devices could provide image quality feedback 

to the operator to enable him to take another impression. Quality areas such as focus, 

gaze, occlusion, presence of reflected objects covering iris etc. could be highlighted 

during the capture process by device (as actionable feedback). This will help operator to 

capture high quality images. 

2. Iris rotation – Device vendors could alert the operators when device is not held straight. 

Images are likely to rotate by certain angle when the devices are not held straight which 

may result in rejects during the matching process. Feedback to operator in this regard is 

likely to help the operator to hold the device straight. Similarly, devices could provide 

feedback when the device is held upside down. 

3. Cameras with mechanical aids - Alignment aid 

– Presence of alignment aids helps operator to position the device for capture. 

– Alignment aid helps to fix the camera on capture area to help position the camera 

correctly. Alignment aid will also help in placing the device at the right capture distance. 

– In the absence of alignment aids, display of live stream from camera will help operator 

to align the device. 

– Alignment aid design should take safety of eyes into account 

– Alignment aid / Enclosure help reduce ambient reflections. This may help accuracy. 

Cameras with Enclosure help reduce ambient reflections. This will also help when the 

capture is taking place in open areas or areas with excess ambient lighting conditions. 

In summary, Cameras with visual indications for capture process, eye alignment and capture 

completion were easiest to use. Cameras that offered backlit LCD display to the operator helped 

operator to capture very quickly. Conversely, cameras that offered no feedback to operator were 

relatively more difficult to use. Dual eye cameras would benefit from alignment visual indication for 

operator. Capture aids help operator and resident during the capture process. 

7.4 Detection and Capture of iris 

It was seen during the POC that certain cameras were able to detect and capture resident iris more 

effectively than others. It was observed that certain special eye conditions where resident had 

undergone possibly surgery (for cataract in most cases) due to which the pupil shape has changed 

had resulted in capture failures in certain authentication cameras whereas certain other 

authentication cameras were able to capture the same. Some of the resident’s iris images which 

were successfully captured on few cameras but failed to capture on others are shown below. 

© UIDAI, 2009-2012 Page 34 of 36


Improvements to detection and capture algorithms after taking note of the nature of iris images 

below is expected to improve the capture efficiency of failing devices. 

Figure 19 Resident with special condition Eyes 

7.5 Dual eye vs. single eye cameras 

During POC, of 8 cameras deployed, 4 cameras were dual iris and 4 were single eye cameras. Of the 

four dual iris cameras deployed, one camera implemented dual simultaneous capture, with left/right 

single-eye capture in succession. 

Dual iris cameras were seen to complete the dual eye capture overall faster than single eye cameras 

where the capture for each iris had to be initiated one eye at a time. Since single eye cameras had 

to capture one eye at a time, and dual eye cameras capture both eyes together. Consequently dual 

eye capture time is less for dual eye cameras. 

However when capturing iris of residents with special eyes, it is possible that one of the iris is good 

and can be captured with high quality. Dual iris devices which look for pair of iris in good conditions 

© UIDAI, 2009-2012 Page 35 of 36


are likely to fail in these situations. Single eye devices have an advantage in these situations where 

resident’s only eye can be captured due to special eye conditions. Dual iris cameras can improve 

capture algorithms to capture resident iris when presented with resident with special eye 

conditions. 

In Summary, both single eye and dual eye devices were found suitable for iris authentication. 

7.6 Capture Distance 

Capture distance refers to distance at which the camera should be held in order to enable the 

capture of iris. As noted in the table above, in the field trial, cameras had capture distance ranging 

from 10mm to over 300mm. 

Higher capture distance in general has convenience and hygiene value as device need not be taken 

very close to eyes during the capture. As capture distance increase, capture depth has to be 

correspondingly increased to ensure ease of capture. 

© UIDAI, 2009-2012 Page 36 of 36

IRIS Authentication Accuracy â€“ PoC Report - uidai

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