Master Thesis - Department of Computer Science

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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
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Page 9 and 10: 3 An Efficient Method of Face Recog
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Genuine Impostor Scores Scores 1 0
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Error d1 = (1 − TZeroF RR) d2 = |
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The algorithm for obtaining the sub
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each subject, 42 samples (flashes f
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Table 3.3: Peak Recognition Accurac
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Table 3.5: Peak Recognition Accurac
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Table 3.7: Performance of Ekenel’
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Table 3.9: Performance of Ekenel’
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to our proposed method. Section 4.2
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plored both of the spaces to captur
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project only the class means in ran
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ΩNull and ΩRange represents the
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Thus, calculation of the QR factori
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4.3.3 Algorithm for Feature Fusion
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The architecture of our proposed me
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T R T S denoted by RVNull and RVNul
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x DNull DRange DP (x) DNull(x) DRan
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C 2 C 1 C 1 C 2 C X X 2 M X M X 2 1
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iii) Project all class means onto n
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Table 4.1: Effect of increasing num
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Table 4.3: Effect of increasing num
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(Original face) (Null face) (Range
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Table 4.6: Performance of Dual Spac
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5.1 Introduction In recent years, t
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This means for a classifier D: r i
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validation1, validation2 and test.
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5.3.1 The Algorithm The steps of ou
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5.4 Experimental Results In this ch
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sets. • We put as much as possibl
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argument is invalid in the context
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LDA and LDA produce the same accura
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space. If the training data uniform
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face for each subject, and compare
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also be introduced. But class speci
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These stages are discussed in the f
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(a) (b) Figure A.2: Input fingerpri
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(a) (b) Figure A.4: Orientation ima
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(i) For each block centered at (i,
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(a) (b) Figure A.7: Enhanced images
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(a) (b) Figure A.10: (a) and (b) sh
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(a) (b) Figure A.12: Binarized imag
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(a) (b) Figure A.14: Thinned images
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(a) (b) Figure A.16: Thinned finger
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Paired Minutiae Minutiae with unmat
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Bibliography [1] FVC 2002 website.
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[25] Li-Fen Chen, Hong-Yuan Mark Li
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[49] Lin Hong, Yifei Wan, and Anil
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[71] L. Lam and C. Y. Suen. Applica
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IEEE Tran. on Pattern Analysis and
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of-the-Art Systems. IEEE Tran. on P
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and Human Communication, pages 374-
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Master Thesis - Department of Computer Science

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