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

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f(u j , v j ) = θ(u j , v j ) = S∑ |∇G σs (u j , v j ) ⊛ Y (u j , v j )| (5.5) s=0 S∑ arg (∇G σs (u j , v j ) ⊛ Y (u j , v j )) , (5.6) s=0 where Y is the luma of the color image I, and G σs is the Gaussian function with zero mean and standard deviation σ s . The largest standard deviation σ S is limited by the distance between eyes and eyebrows where S = 4, and ∇ and ⊛ are the gradient and convolution operators. The gradient magnitude, gradient orientation, eye map [175] and coarse alignment results for the subject in Fig. 5.4(a) are shown in Fig. 5.5. The eye map is an average of a symmetry map [177] and an eye energy map (will be explained in Section 5.3.1). Furthermore, we construct a shadow map of a face image in order to locate eyebrow, nostril, and mouth lines, based on the average value of luminance intensity on a facial skin region (i.e., rectangles shown in Figs. 5.6(a) and 5.6(c)). These feature lines, shown as dark lines in Figs. 5.7(c), are used to adjust corresponding facial components of a semantic graph. Fig. 5.7 shows five examples of coarse alignment. 5.3 Fine Alignment of Semantic Face Graph via Interacting Snakes Fine alignment employs active contours to locally refine facial components of a semantic face graph that is drawn from a 3D generic face model. The 2D projection of 118
(a) (b) (c) (d) Figure 5.5. Boundary map and eye component map for coarse alignment: (a) and (b) are gradient magnitude and orientation, respectively, obtained from multi-scale Gaussian-blurred edge response; (c) an eye map extracted from a face image shown in Fig. 5.4(c); (d) a semantic face graph overlaid on a 3D plot of the eye map; (e) image overlaid with a coarsely aligned face graph. (e) a semantic face graph produces a collection of component boundaries, each of which is described by a closed (or open) active contour. The collection of these active contours, called interacting snakes, interact with each other through a repulsion energy in order to align the general facial topology onto the sensed face images in an iterative fashion. We have studied two competing implementations of active contours for the deformation of interacting snakes: (i) explicit (or parametric) and (ii) implicit contour 119
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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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Page 87 and 88: 3.3 Localization of Facial Features
Page 89 and 90: (a) (b) (c) Figure 3.9. Constructio
Page 91 and 92: 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
Page 103 and 104: (a) (b) (c) (d) (e) Figure 3.18. Fa
Page 105 and 106: The number of false positives is al
Page 107 and 108: (a) (b) (c) (d) Figure 3.20. Face d
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 139 and 140: (a) (b) (c) (d) Figure 5.7. Coarse
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Page 149 and 150: (a) (b) (c) (d) (e) (f) Figure 5.14
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Page 155 and 156: (a) (b) (c) (d) Figure 5.18. Exampl
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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
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Page 185 and 186: Bibliography [1] Visionics Corporat
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[32] L. Wiskott, J.M. Fellous, N. K
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[59] M. Grudin, “On internal repr
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[84] M. Abdel-Mottaleb and A. Elgam
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[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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