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

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(a) (b) (c) (d) Figure 3.21. Face detection results on a subset of the Champion database: (a) input images; (b) grouped skin regions; (c) face candidates; (d) detected faces are overlaid on the lighting-compensated images. 90
Figure 3.22. Face detection results on a subset of eleven family photos. Each image contains multiple human faces. The detected faces are overlaid on the colorcompensated images. False negatives are due to extreme lighting conditions and shadows. Notice the difference between the input and color-compensated images in terms of color balance. The bias color in the original images has been compensated in the resultant images. 91
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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
Page 57 and 58: esult in different recognition appr
Page 59 and 60: (a) (b) (c) Figure 2.2. Examples of
Page 61 and 62: (a) (b) (c) (d) Figure 2.4. Interna
Page 63 and 64: Figure 2.6. A breakdown of face rec
Page 65 and 66: 2.3 Face Modeling Face modeling pla
Page 67 and 68: An advanced modeling approach which
Page 69 and 70: tionals to minimize, implementation
Page 71 and 72: are listed in Table 2.2. All of the
Page 73 and 74: low-level features can provide mean
Page 75 and 76: holistic 2D and geometrical 3D feat
Page 77 and 78: size) to generate potential face ca
Page 79 and 80: 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
Page 91 and 92: The eye map from the chroma is enha
Page 93 and 94: mouth is performed within the face
Page 95 and 96: Figure 3.12. Computation of face bo
Page 97 and 98: center, and lengths of major and mi
Page 99 and 100: segment. The other term favors an u
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
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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
Page 117 and 118: and nose in frontal views), and bec
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
Page 131 and 132: (a) (b) (c) Figure 5.1. Semantic fa
Page 133 and 134: of Fourier series truncation. In ad
Page 135 and 136: ponent color. The semantic facial s
Page 137 and 138: (a) (b) (c) (d) Figure 5.5. Boundar
Page 139 and 140: (a) (b) (c) (d) Figure 5.7. Coarse
Page 141 and 142: In the Baysian framework, given an
Page 143 and 144: edge map; hence it requires a clean
Page 145 and 146: shows examples of mouth energies. F
Page 147 and 148: [ ( ∂Φ ∂t =∇Φ µ 1 div(g
Page 149 and 150: (a) (b) (c) (d) (e) (f) Figure 5.14
Page 151 and 152: y the reliability of component matc
Page 153 and 154: tation, face size, and lighting con
Page 155 and 156: (a) (b) (c) (d) Figure 5.18. Exampl
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(a) (b) (c) (d) (e) (f) (g) Figure
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5.6 Summary For overcoming variatio
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such vision-based systems are (i) d
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6.2 Future Directions Based on the
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(a) (b) (c) (d) Figure 6.2. An exam
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• Non-frontal training views: Acc
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Appendices
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⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ Y
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and are shown as blue-dashed lines
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adius of a cluster ‘i’ w.r.t. a
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Appendix C Image Processing Templat
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considering pixel classes as voxel
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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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