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

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10 5 Z 8 6 4 2 −4 −2024 X 5 0 −5 Y X (a) (b) (c) Y 0 −5 −5 0 5 Figure 5.2. 3D generic face model: (a) Waters’ triangular-mesh model shown in the side view; (b) model in (a) overlaid with facial curves including hair and ears at a side view; (c) model in (b) shown in the frontal view. for facial component i with a close boundary such as eyes and mouth, and with endvertex padding for those having open boundary such as ears and hair components. The advantage of using semantic graph descriptors for face matching is that these descriptors can seamlessly encode geometric relationships (scaling, rotation, translation, and shearing) among facial components in a compact format in the spatial frequency domain, because the vertices of all the facial components are specified in the same coordinate system with the origin around the nose (see Fig. 5.2). The reconstruction of semantic face graphs from semantic graph descriptors is obtained by ũ i (n) = F −1 {a i (k)} = L∑ i −1 k=0 a i (k) · e j2πkn/N i , (5.2) where L i (< N i ) is the number of frequency components used for the i th face component. Figure 5.3 shows the reconstructed semantic face graphs at different levels 114
of Fourier series truncation. In addition, the coordinates of component boundary (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) Figure 5.3. Semantic face graphs for the frontal view are reconstructed using Fourier descriptors with spatial frequency coefficients increasing from (a) 10% to (j) 100% at increments of 10%. of G can also be represented by parametric curves, i.e., c(s) = (x(s), y(s)), where s ∈ [0, 1], for explicit curve deformation or for generating level-set functions for implicit curve evolution. Therefore, the component boundaries of a semantic face graph are associated with a collection of active contours (snakes). 5.2 Coarse Alignment of Semantic Face Graph Our face recognition system contains four major modules: face detection, pose estimation, face alignment, and face matching. The face detection module finds locations of face and facial features in a color image using the algorithm in [175]. Figures 5.4(a) to 5.4(d) show input color images and the results of face detection. Currently, we as- 115
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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
Page 81 and 82: normalized red-green (r-g) space [1
Page 83 and 84: (a) (b) Figure 3.3. The Y C b C r c
Page 85 and 86: (a) Figure 3.5. 2D projections of t
Page 87 and 88: 3.3 Localization of Facial Features
Page 89 and 90: (a) (b) (c) Figure 3.9. Constructio
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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
Page 107 and 108: (a) (b) (c) (d) Figure 3.20. Face d
Page 109 and 110: Figure 3.22. Face detection results
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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
Page 123 and 124: a feature-dependent decay factor. F
Page 125 and 126: is to find appropriate external ene
Page 127 and 128: (a) (b) (d) (e) (f) Figure 4.11. Te
Page 129 and 130: Chapter 5 Semantic Face Recognition
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Page 137 and 138: (a) (b) (c) (d) Figure 5.5. Boundar
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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
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gray8imageA(120,120) = 200; // Asse
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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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