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

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2 Biometric system design 2.4 State-of-the-art in Hand biometrics Concerning the hand-based recognition part of this work, not only a single biometric modality is of relevance, but three common modalities extracted out of geometric and textural physiological properties of the human hand [14]: • hand geometry extracting local measurements (such as finger lengths, palm width, etc.) out of a (binary) image of the whole hand; • palmprint focusing on position and orientation of principal lines and; • fingerprint extracting the flow of ridge patterns in the tip of the finger. Additionally, palm vein patterns extracted from infrared palmar images were introduced in 2002 [44] as a new distinct type of biometric authentication. All techniques have distinct biometric properties and are aimed at different applications. Different sensing devices for each of the modalities have emerged [21, 44]. The human hand soon appealed to biometricians due to its high accessibility. In the meantime, more sophisticated techniques have emerged yielding extremely low EERs (e.g. iris recognition systems), however hand-based features are still one of the most common biometrics used today. While a variety of different hand-biometric systems (such as [18, 35, 42]) already exist, whole-hand-based systems, scanning the entire hand to extract multiple features, are not yet common. The fusion of hand geometry, palmprint and fingerprint biometrics in a single sensor environment has been proposed by [19] and an implemented solution has recently been presented by [34]. Nevertheless no implementation is known to the author working with commercially available scanners so far. However, results using randomly paired samples, as is done in [19] seem promising. Using simple fusion techniques, recognition rates of single biometrics with EERs of 8.54% (hand shape), 11.06% (palmprint) and 11.4% (fingerprint) could be improved to 3.53% (hand shape + palmprint + fingerprint). Using multi-spectral scanning methods [34], even higher accuracy can be achieved. However, unfortunately the lack of standardised testing conditions regarding population size, performance indicators, time lapse between recordings, and even operation modes makes fair comparisons between hand geometry, palmprint and fingerprint systems difficult. Typical state-of-the-art error rates associated with the introduced techniques can be found in Table 2.2. It should be clear that error rates also heavily depend on the type (and supported area) of input sensor used (e.g. thermal, optical or capacitative in case of fingerprints) and whether the algorithms performance tests have been executed by third parties. For example, in the FVC2006 [50, 3] the best reported EER results in the open category are 0.021% EER for the optical sensor versus 5.564% for a different database with an electrical field sensor. This implies, that it is even more difficult to compare different biometrics. Therefore, the conducted performance comparison in this work is fair in terms of sensor (all feature extractors are provided with the same sensed information and may discard different parts of the hand) and population (all tests refer to the same group of users). 14
2.4 State-of-the-art in Hand biometrics Type Reference Description Samples FMR FNMR Indicator Geometry Geometry Palmprint Kumar et al. [18] Yoruk et al. [43] Kumar et al. [18] Palmprint Zhang [44] Palmprint Zhang [44] Fingerprint FVC2006 DB2 open winner [50] 4 finger lengths, 8 finger widths, palm width, palm length, hand area, and hand length independent component features of the hand silhouette images standard deviation of greylevels in 144 overlapping blocks phase information of discrete Gabor Filter convoluted with palmprint subimage projection of 128 × 128 sub-image onto eigenspace spanned by 100 eigenvectors (Eigenpalm approach) anonymous algorithm on 400 × 560 (569 dpi) optical sensor data (BiometriKa) 1000 5.29% 8.34% MinHTER 458 2.77% 2.77% EER 1000 4.49% 2.04% MinHTER 425 0% 2.5% ZeroFMR 3056 0.03% 1% FMR ≈ 0 1680 0.02% 0.02% EER Table 2.2: Error rates of recent hand-based biometric systems in verification mode. All hand-based systems have the following major limitations in common: Firstly, most systems are overt, i.e. acquisition is an evident and cooperative process. Secondly, capture takes place with the subject nearby and often well-defined environmental conditions (such as e.g. lighting) are required. Finally, limitations concerning universality exist. The inability to acquire features may be caused by serious infringement (e.g. land mines), congenital physical anomalies (e.g. polydactyly causing supernumerary fingers or dermatopathia pigmentosa, a disorder causing a lack of fingerprints), or even inappropriate hardware unable to deal with extreme shape. 2.4.1 Hand geometry Hand geometry systems have been implemented since the early 1970s [13] and target the extraction of the silhouette shape of the human hand or single fingers, finger lengths and local widths. Measurements can easily be extracted from low quality scans or camera images of the hand with resolutions starting at 45 dpi [43], as no textural information is involved. Shape-based features are invariant under environmental factors such as lighting conditions, sweat, dry skin or small injuries, however they may change over larger time spans, especially during growth [14]. Another difficulty is constituted by the physical anatomy of the human hand. If fingers touch each other, salient points [28] needed for 15
Page 1: Single-sensor hand and footprint-ba
Page 5 and 6: Contents 1 Introduction 1 1.1 Assig
Page 7 and 8: 1 Introduction According to [10], t
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
Page 16 and 17: 2 Biometric system design 2.2 Syste
Page 18 and 19: 2 Biometric system design probabili
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
Page 45 and 46: i=1 j=1 m� n� 4.3 Rotational Al
Page 47 and 48: value 1000 500 0 -500 4.5 Salient p
Page 49 and 50: 4.5 Salient point detection and pea
Page 51 and 52: 5 Feature extraction The designed h
Page 53 and 54: Algorithm Hand Foot Silhouette Shap
Page 55 and 56: 5.2 Silhouette feature Using feet i
Page 57 and 58: 5.3 Shape feature separately. At ma
Page 59 and 60: 5.4 Fingerlength and Toelength feat
Page 61 and 62: 5.5 Palmprint and Soleprint feature
Page 63 and 64: 5.6 Eigenpalms, Eigenfingers and Ei
Page 65 and 66: 5.6 Eigenpalms, Eigenfingers and Ei
Page 67 and 68: y-axis x1 y1 1 Bifurcation (bif,x1,
Page 69: 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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