Single-sensor hand and footprint-based multimodal biometric ...

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Contents 4.4 Silhouette extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.5 Salient point detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.5.1 Salient point refinement for hands . . . . . . . . . . . . . . . . . . . 42 4.5.2 Salient point refinement for feet . . . . . . . . . . . . . . . . . . . . 43 5 Feature extraction 45 5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.1.1 Geometric features . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.1.2 Texture-based features . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2 Silhouette feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.3 Shape feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.4 Fingerlength and Toelength feature . . . . . . . . . . . . . . . . . . . . . . 53 5.5 Palmprint and Soleprint feature . . . . . . . . . . . . . . . . . . . . . . . . 55 5.6 Eigenpalms, Eigenfingers and Eigenfeet feature . . . . . . . . . . . . . . . . 57 5.7 Minutiae feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 6 Matching and Decision 65 6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 6.2 Minutiae matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.3 Dynamic time warp matching . . . . . . . . . . . . . . . . . . . . . . . . . 68 6.4 Score level fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6.5 Decision level fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 7 Experiments 73 7.1 Test setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 7.1.1 Test databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 7.1.2 Acquisition protocol . . . . . . . . . . . . . . . . . . . . . . . . . . 75 7.2 Hand matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 7.2.1 Verification performance . . . . . . . . . . . . . . . . . . . . . . . . 76 7.2.2 Identification performance . . . . . . . . . . . . . . . . . . . . . . . 81 7.3 Foot matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 7.3.1 Verification performance . . . . . . . . . . . . . . . . . . . . . . . . 83 7.3.2 Identification performance . . . . . . . . . . . . . . . . . . . . . . . 85 7.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 7.4.1 A note on statistical significance . . . . . . . . . . . . . . . . . . . . 87 8 Summary 89 Bibliography 91 VI
1 Introduction According to [10], the task of biometric (from Greek bios-life, metron-measure) systems consists of determining the personal identity based on his or her distinguishing physiological and/or behavioural characteristics (in the majority of cases both properties are addressed [21]). While the term “biometric” may also refer more generally to the application of mathematics and especially statistics to biological sciences, this work explicitly concentrates on the information technologic aspect focusing on authentication tasks. In order to recognise a person, a biometric software extracts a machine-readable representation of physiological and/or behavioural characteristics of that person, called feature vector or template [52], to facilitate score-based matching with stored templates in member databases. From this point of view, biometry is an important pattern recognition research problem [11]. Using hand or footprint-based measurements constitutes one of many different possibilities to realise biometric authentication. The term multimodal in the title implies that multiple evidences of the same identity are provided using not only a single biometric indicator but merging the information of multiple matchers in order to improve accuracy [32]. Biometric fusion is a common method to cope with unsatisfactory recognition rates or performance of unimodal systems [21]. While most multimodal biometric systems incorporate multiple sensors, this work operates on optical single-sensor output and may therefore be classified as a fusion system integrating multiple representations and matching algorithms for the same biometric, according to [14]. Finally, the term recognition in this work refers to both verification (1 : 1 comparison with a claimed identity template) and identification (1 : n comparison to find a matching template, if existing), as is used in respective literature [1, 21]. The research field of biometrics has seen an enormous growth in the last decade. Thus, numerous biometric identifiers emerged exhibiting certain strengths and weaknesses with respect to specific biometric applications. Whilst biometric patterns (fingerprint carvings) are present on archeological artifacts of the Neolithic age (8000 − 7500 B.C.) [21], modern biometric literature has its roots in criminal investigations with Alphonse Bertillon being the first to use physiological measurements to identify humans in the late 19th century [11]. In fact many of the techniques employed in biometric systems today, like fingerprint classification or the well-known minutiae features in fingerprints by Francis Galton, date back to the industrial revolution period of the late 18th and early 19th centuries [21]. Finally, another milestone for biometrics was constituted by the development of the automatic fingerprint identification system (AFIS) in the early 1960s [21]. Today, criminal applications are just one example where biometric measurements are employed. The market of civilian and governmental applications is constantly growing, as can be seen from the Biometric Market Report conducted by the International Biometric Group [47]: the 1
Page 1: Single-sensor hand and footprint-ba
Page 5: Contents 1 Introduction 1 1.1 Assig
Page 9 and 10: 1.2 Motivation or passwords, for ex
Page 11: 1.5 Notations used An overview of m
Page 14 and 15: 2 Biometric system design Wellness
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Page 18 and 19: 2 Biometric system design probabili
Page 20 and 21: 2 Biometric system design 2.4 State
Page 22 and 23: 2 Biometric system design reliable
Page 24 and 25: 2 Biometric system design Type Refe
Page 26 and 27: 2 Biometric system design • at th
Page 28 and 29: 3 Sensing using under-water image c
Page 30 and 31: 3 Sensing than valleys filled with
Page 32 and 33: 3 Sensing duration (sec) 600 550 50
Page 35 and 36: 4 Preprocessing While preprocessing
Page 37 and 38: 4.1 Overview with line-approximatio
Page 39 and 40: 4.1.2 Foot preprocessing 4.1 Overvi
Page 41 and 42: (a) Detail in original scan. (b) De
Page 43 and 44: 4.3 Rotational Alignment where B
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Page 47 and 48: value 1000 500 0 -500 4.5 Salient p
Page 49 and 50: 4.5 Salient point detection and pea
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Page 53 and 54: Algorithm Hand Foot Silhouette Shap
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5.3 Shape feature separately. At ma
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5.4 Fingerlength and Toelength feat
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5.5 Palmprint and Soleprint feature
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5.6 Eigenpalms, Eigenfingers and Ei
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5.6 Eigenpalms, Eigenfingers and Ei
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y-axis x1 y1 1 Bifurcation (bif,x1,
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5.7 Minutiae feature therefore seve
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6 Matching and Decision decision mo
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6 Matching and Decision of minutiae
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6 Matching and Decision Given k ∈
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6 Matching and Decision In case of
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7 Experiments - Prepaid architectur
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7 Experiments 3. Scanning process:
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7 Experiments relative frequency re
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7 Experiments sation parameters), I
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7 Experiments false non-match rate
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7 Experiments relative frequency re
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7 Experiments distance to Minutiae
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7 Experiments Rule of 3 [22]: accor
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8 Summary footprints outperformed e
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Bibliography [13] A. K. Jain, A. Ro
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Bibliography [42] E. Yörük, H. Du
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