Master Thesis - Department of Computer Science

More documents

Recommendations

Info

1.2 Multimodal Biometric System There are a number of problems involved in a unimodal biometric systems such as non-universality, spoof attacks, etc. These drive the research community to think about more robust and secured biometric system which will be more difficult to fool than a system based on a single biometric. The first choice is obviously a multimodal biometric system which uses multiple biometric traits to offer robust decision-making. The main motivations behind multimodal biometric system are the limitation of unimodal biometric systems in the following ways: • Noisy sensed data: Unimodal biometric systems are very much sensitive to the trait they use. Noisy sensed data affects the performance of a system by getting incorrectly matched with templates in the database. Fingerprint with scar or voice altered by cold, accumulation of dirt or residual fingerprint on fingerprint sensor can result in noisy data. • Intra-class variations: “Intra-class variation” can be explained by the fact that the biometric data acquired during verification will not be identical to the data used for generating user’s template during enrollment due to various factors. In case of face, slight change in pose or ambient light and use of spectacles may cause the face to appear different from its template. • Lack of individuality: Features from same biometric trait from different individuals can be quite similar. For face biometric the facial features like, eyes, nose, mouth are in right place for everybody. Moreover, twins or father and son can be identical in facial appearance due to genetic factors. This lack of uniqueness increases the False Acceptance Rate (FAR) of a biometric system. • Non-universality: No biometric is truly universal. For example, fingerprints are supposedly universal, but there is a small fraction of the population that does not possess fingerprint due to hand-related disabilities that are unsuitable for automatic matching. Thus, there is a failure to enroll (FTE) rate associated with using a single biometric trait. It has been empirically estimated that around 4% of fingerprint images have poor quality ridges. 4
• Spoof Attacks: An impostor may attempt to spoof the biometric trait of a legitimate enrolled user in order to circumvent the system. This type of attacks are well-known in case of behavioral traits like signature and voice. Even it is possible to construct dummy fingers using lifted fingerprint impressions. Multimodal biometry solves the above defined problems by combining the evidences obtained from different modalities with the help of an effective fusion scheme. An alternate use of multimodal biometry is to perform a search in an efficient and faster way by using a relatively simple and less accurate modality to prune the database be- fore using the more complex and accurate modality on the remaining data to generate the final decision. There are, however, a few disadvantages of using a multimodal biometric system. First, a multimodal biometric system is more expensive and requires more computational and storage resources than a unimodal system. Second, multimodal systems generally require more time for enrollment and verification, causing some inconveniences to the user. Furthermore, if the multiple modalities are not properly combined, the combination may actually degrade a system accuracy. 1.3 Brief Description of the Work Done The contribution of the work presented in this thesis comes in terms of three novel face recognition approaches and one decision fusion technique for combining information from face and fingerprint classifiers for multimodal biometry. • Among three face recognition approaches, the first approach presents an efficient method for frontal face recognition, using subject-specific subband face representation. The human face has certain visual features that are common among everybody and some others that exhibit the unique characteristics of an individual. Using the discrete wavelet transform (DWT), we extract these unique features from the face image for discriminating it from others. The face image is decomposed into several subbands to separate the common (approximation) and discriminatory (detail) parts. Subband face is reconstructed from selected wavelet subbands, in which a suitable approximation subband is 5
Page 1 and 2: DEVELOPMENT OF EFFICIENT METHODS FO
Page 3 and 4: To My Parents, Sisters and Dearest
Page 5 and 6: Mirnalinee, Shreyasee, Lalit, Manis
Page 7 and 8: LDC Linear Discriminant Classifier
Page 9 and 10: 3 An Efficient Method of Face Recog
Page 11 and 12: A.6 Minutiae Matching . . . . . . .
Page 13 and 14: 4.1 Effect of increasing number of
Page 15 and 16: List of Figures 2.1 Summary of appr
Page 17 and 18: 3.7 Area difference between genuine
Page 19 and 20: Abstract Biometrics is a rapidly ev
Page 21 and 22: CHAPTER 1 Introduction The issues a
Page 23: 1.1.1 Applications Biometrics has b
Page 27 and 28: • A new approach for multimodal b
Page 29 and 30: CHAPTER 2 Literature Review This re
Page 31 and 32: ange [23], infrared scanned [137] a
Page 33 and 34: u 2 x 2 u 1 x 1 (a) PCA basis (b) P
Page 35 and 36: PCA Projection Class 1 Class 2 LDA
Page 37 and 38: F DIFS DFFS F (a) (b) F 1 L Figure
Page 39 and 40: image A of m rows and n columns is
Page 41 and 42: faces of 40 subjects. • Others: A
Page 43 and 44: malizing the vector components, the
Page 45 and 46: Figure 2.5: A fingerprint image wit
Page 47 and 48: features (gradient coherence, inten
Page 49 and 50: Figure 2.7: Examples of minutiae; A
Page 51 and 52: according to the local orientation
Page 53 and 54: • Parallel Mode: This operational
Page 55 and 56: can address the problem of noisy se
Page 57 and 58: Prior to Matching Sensor Level Feat
Page 59 and 60: determined by logistic regression.
Page 61 and 62: Class-indifferent Methods • Decis
Page 63 and 64: 3. The final DS soft vector is calc
Page 65 and 66: on three face databases and compare
Page 67 and 68: for a face. This was illustrated by
Page 69 and 70: Image Rows h(.) g(.) 2 2 Columns Fi
Page 71 and 72: in the accuracy of face recognition
Page 73 and 74: (a) (a1) (a2) (a3) (b) (b1) (b2) (b
Page 75 and 76:
Genuine Impostor Scores Scores 1 0
Page 77 and 78:
Error d1 = (1 − TZeroF RR) d2 = |
Page 79 and 80:
The algorithm for obtaining the sub
Page 81 and 82:
each subject, 42 samples (flashes f
Page 83 and 84:
Table 3.3: Peak Recognition Accurac
Page 85 and 86:
Table 3.5: Peak Recognition Accurac
Page 87 and 88:
Table 3.7: Performance of Ekenel’
Page 89 and 90:
Table 3.9: Performance of Ekenel’
Page 91 and 92:
to our proposed method. Section 4.2
Page 93 and 94:
plored both of the spaces to captur
Page 95 and 96:
project only the class means in ran
Page 97 and 98:
ΩNull and ΩRange represents the
Page 99 and 100:
Thus, calculation of the QR factori
Page 101 and 102:
4.3.3 Algorithm for Feature Fusion
Page 103 and 104:
The architecture of our proposed me
Page 105 and 106:
T R T S denoted by RVNull and RVNul
Page 107 and 108:
x DNull DRange DP (x) DNull(x) DRan
Page 109 and 110:
C 2 C 1 C 1 C 2 C X X 2 M X M X 2 1
Page 111 and 112:
iii) Project all class means onto n
Page 113 and 114:
Table 4.1: Effect of increasing num
Page 115 and 116:
Table 4.3: Effect of increasing num
Page 117 and 118:
(Original face) (Null face) (Range
Page 119 and 120:
Table 4.6: Performance of Dual Spac
Page 121 and 122:
5.1 Introduction In recent years, t
Page 123 and 124:
This means for a classifier D: r i
Page 125 and 126:
validation1, validation2 and test.
Page 127 and 128:
5.3.1 The Algorithm The steps of ou
Page 129 and 130:
5.4 Experimental Results In this ch
Page 131 and 132:
sets. • We put as much as possibl
Page 133 and 134:
argument is invalid in the context
Page 135 and 136:
LDA and LDA produce the same accura
Page 137 and 138:
space. If the training data uniform
Page 139 and 140:
face for each subject, and compare
Page 141 and 142:
also be introduced. But class speci
Page 143 and 144:
These stages are discussed in the f
Page 145 and 146:
(a) (b) Figure A.2: Input fingerpri
Page 147 and 148:
(a) (b) Figure A.4: Orientation ima
Page 149 and 150:
(i) For each block centered at (i,
Page 151 and 152:
(a) (b) Figure A.7: Enhanced images
Page 153 and 154:
(a) (b) Figure A.10: (a) and (b) sh
Page 155 and 156:
(a) (b) Figure A.12: Binarized imag
Page 157 and 158:
(a) (b) Figure A.14: Thinned images
Page 159 and 160:
(a) (b) Figure A.16: Thinned finger
Page 161 and 162:
Paired Minutiae Minutiae with unmat
Page 163 and 164:
Bibliography [1] FVC 2002 website.
Page 165 and 166:
[25] Li-Fen Chen, Hong-Yuan Mark Li
Page 167 and 168:
[49] Lin Hong, Yifei Wan, and Anil
Page 169 and 170:
[71] L. Lam and C. Y. Suen. Applica
Page 171 and 172:
IEEE Tran. on Pattern Analysis and
Page 173 and 174:
of-the-Art Systems. IEEE Tran. on P
Page 175 and 176:
and Human Communication, pages 374-
show all

Master Thesis - Department of Computer Science

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?