Face Detection and Modeling for Recognition - Biometrics Research ...

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E comp eye = E msat + E csh + E cdif , (5.12) ( E msat = [ (R − K 3 )2 + (G − K 3 )2 + (B − K 3 )2 ) 0.5 (R + G + B − K)2 − ], 3 (5.13) E csh = [[Cr − K/2] 2 − [Cb − K/2] 2 ], (5.14) E cdif = [[Cr] − [Cb]], (5.15) where E msat is the modified saturation (that is the distance in the plane between a point (R, G, B) and (K/3, K/3, K/3)) where R + G + B = K, E csh is chroma shift, E cdif is chroma difference, K = 256 is the number of grayscales for each color component, and [x] indicates a function that normalizes x into the interval [0, 1]. The eye component energies for subjects in Fig. 5.11(a) is shown in Fig. 5.11(b). The (a) (b) (c) (d) (e) Figure 5.11. Component energy (darker pixels have stronger energy): (a) face region of interest; (b) eye component energy; (c) mouth component energy; (d) nose boundary energy; (e) nose boundary energy shown as a 3D mesh surface. mouth component energy is computed as E comp mouth 126 = [−[Cb] − [Cr]]. Figure 5.11(c)
shows examples of mouth energies. For the nose component, its GVF is usually weak, and it is difficult to construct an energy map for nose. Hence, for the nose, we utilize Tsai and Shah’s shape-from-shading (SFS) algorithm [179] to generate a boundary energy for augmenting the GVF for the nose component. The illumination direction used in the SFS algorithm is estimated from the average gradient fields of a face image [180] within a facial region. Figures 5.11(d) and 5.11(e) show examples of nose boundary energies in a 2D grayscale image and a 3D mesh plot, respectively. 5.3.2 Parametric Active Contours Once we obtain the attraction force, we can make use of the implicit finite differential method [77], [127] and the iteratively updated repulsion force to deform the snakes. The stopping criteria is based on the iterative movement of each snake. Figure 5.12(a) shows the initial interacting snakes, Fig. 5.12(b) shows snake deformation without the eyebrow snakes, and Fig. 5.12(c) shows finely aligned snakes. Component matching scores in Eq. (5.4) are then updated based on the line and region integrals of boundary and component energies, respectively. We discuss another approach for deforming the interacting snakes based on geodesic active contours and level-set functions in Section 5.3.3. 5.3.3 Geodesic Active Contours As implicit contours, geodesic snakes [181], which employ level-sets functions [182] are designed for extracting complex geometry. We initialize a level-set function using 127
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Face Detection and Modeling for Rec
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perimental results demonstrate succ
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To my parents; my lovely wife, Pei-
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for providing the range datasets; t
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4 Face Modeling 97 4.1 Modeling Met
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List of Figures 1.1 Applications us
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2.6 A breakdown of face recognition
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4.2 3D triangular-mesh model and it
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5.14 Fine alignment using geodesic
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Chapter 1 Introduction In recent ye
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(a) (b) Figure 1.1. Applications us
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(e) Figure 1.1. (Cont’d). (f) (a)
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esolutions and of poor quality (i.e
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can recognize known faces in carica
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the motivation for studies that att
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(a) Figure 1.10. Similarity of fron
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verify the face present in an image
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17 Figure 1.12. System diagram of o
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algorithm can also provide geometri
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commercial parametric face modeling
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1.5.1 Face Alignment Using 2.5D Sna
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1.5.3 Face Alignment Using Interact
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Using merely low-level features (e.
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such as eyes, mouth and face bounda
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can be applied to a 3D face model w
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Chapter 2 Literature Review We firs
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mation theory, geometrical modeling
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(e) (f) (g) (h) (i) (j) (k) (l) Fig
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esult in different recognition appr
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(a) (b) (c) Figure 2.2. Examples of
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(a) (b) (c) (d) Figure 2.4. Interna
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Figure 2.6. A breakdown of face rec
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2.3 Face Modeling Face modeling pla
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An advanced modeling approach which
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tionals to minimize, implementation
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are listed in Table 2.2. All of the
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low-level features can provide mean
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holistic 2D and geometrical 3D feat
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size) to generate potential face ca
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oundary. A face score is computed f
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normalized red-green (r-g) space [1
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(a) (b) Figure 3.3. The Y C b C r c
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(a) Figure 3.5. 2D projections of t
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3.3 Localization of Facial Features
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(a) (b) (c) Figure 3.9. Constructio
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The eye map from the chroma is enha
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Page 95 and 96: Figure 3.12. Computation of face bo
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Page 101 and 102: (a) (b) (c) (d) (e) Figure 3.15. Fa
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Page 105 and 106: The number of false positives is al
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Page 109 and 110: Figure 3.22. Face detection results
Page 111 and 112: Figure 3.22. (Cont’d). 93
Page 113 and 114: 3.5 Summary We have presented a fac
Page 115 and 116: Chapter 4 Face Modeling We first in
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Page 119 and 120: 4.3 Facial Measurements Facial meas
Page 121 and 122: 4.4 Model Construction Our face mod
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Page 127 and 128: (a) (b) (d) (e) (f) Figure 4.11. Te
Page 129 and 130: Chapter 5 Semantic Face Recognition
Page 131 and 132: (a) (b) (c) Figure 5.1. Semantic fa
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Page 143: edge map; hence it requires a clean
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Page 151 and 152: y the reliability of component matc
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Page 161 and 162: 5.6 Summary For overcoming variatio
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Page 165 and 166: 6.2 Future Directions Based on the
Page 167 and 168: (a) (b) (c) (d) Figure 6.2. An exam
Page 169 and 170: • Non-frontal training views: Acc
Page 171 and 172: Appendices
Page 173 and 174: ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ Y
Page 175 and 176: and are shown as blue-dashed lines
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Page 179 and 180: Appendix C Image Processing Templat
Page 181 and 182: considering pixel classes as voxel
Page 183 and 184: gray8imageA(120,120) = 200; // Asse
Page 185 and 186: Bibliography [1] Visionics Corporat
Page 187 and 188: [32] L. Wiskott, J.M. Fellous, N. K
Page 189 and 190: [59] M. Grudin, “On internal repr
Page 191 and 192: [84] M. Abdel-Mottaleb and A. Elgam
Page 193 and 194: [113] B. Kim and P. Burger, “Dept
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[141] M.D. Adams and F. Kossentini,
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[169] P.T. Jackway and M. Deriche,
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