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

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4 Preprocessing 38 (a) Examples of rotationally aligned hands using moments. (b) Examples of rotationally aligned feet using moments. Figure 4.2: Results of moment-based rotational alignment on hand and foot images.
i=1 j=1 m� n� 4.3 Rotational Alignment Θ = 1 2 arctan( 2µ1,1 ); µ2,0 − µ0,2 (4.9) µ2,0 = m� n� (xij − x) 2 B(i, j); (4.10) µ1,1 = µ0,2 = i=1 j=1 m� n� i=1 j=1 (xij − x)(yij − y)B(i, j); (4.11) (yij − y) 2 B(i, j). (4.12) 4.3.2 Palmprint-boundary based alignment Another technique used for aligning inked palmprints is a definition of a palmprint coordinate system and rotating and translating the palmprint to a located origin and determined y-axis [44]. This method is adapted to binary hand images in this work and involves the following steps: 1. Determination of the origin: while inked palmprints exhibit texture information permitting the definition of the origin as the outer end-point of the heart line [44], binary hand images allow the definition of a similar invariant point using geometrical measures, i.e. the intra-finger valley V2 ∈ R 2 between index and middle finger. 2. Determination of the y-axis: in analogous manner to [44], the y-axis is defined as the outer palm’s boundary (which can be approximated by a straight line) translated to the origin. Start and end points, however have to be defined carefully in order to get the same portion of the outer boundary for different hand-prints. The palm boundary sequence is approximated by a line using the method of least squares in order to obtain a normalised orientation. Definition 4.3.2 (Palm boundary sequence). Let T5 ∈ R 2 denote the finger-tip of the little finger and V4 ∈ R 2 the corresponding intra-finger valley. Furthermore, let 〈S0, . . . , Sn〉 denote the (counter-clockwise) hand silhouette sequence. Then, the palm boundary sequence is the subsequence 〈Sk, . . . Sl〉 with: k = max{x ∈ {0, . . . , n} : � Sx − T5 �≤� V4 − T5 �}; l = max{x ∈ {0, . . . , n} : � Sx − Sk �≤ t}; where t denotes the average length of fingers for this hand. 39
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 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: 4.3 Rotational Alignment where B
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
Page 72 and 73: 6 Matching and Decision decision mo
Page 74 and 75: 6 Matching and Decision of minutiae
Page 76 and 77: 6 Matching and Decision Given k ∈
Page 78 and 79: 6 Matching and Decision In case of
Page 80 and 81: 7 Experiments - Prepaid architectur
Page 82 and 83: 7 Experiments 3. Scanning process:
Page 84 and 85: 7 Experiments relative frequency re
Page 86 and 87: 7 Experiments sation parameters), I
Page 88 and 89: 7 Experiments false non-match rate
Page 90 and 91: 7 Experiments relative frequency re
Page 92 and 93: 7 Experiments distance to Minutiae
Page 94 and 95:
7 Experiments Rule of 3 [22]: accor
Page 96 and 97:
8 Summary footprints outperformed e
Page 98 and 99:
Bibliography [13] A. K. Jain, A. Ro
Page 100:
Bibliography [42] E. Yörük, H. Du
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