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

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(a) (b) (c) (d) Figure 5.6. Shadow maps: (a) and (c) are luma components of face images in Figs. 5.4(a) and 5.4(c), overlaid with rectangles within which the average values of skin intensity is calculated; (b) and (d) are shadow maps where bright pixels indicate the regions that are darker than average skin intensity. representations. The explicit contour representation has the advantage of maintaining the geometric topology. The implicit contour representation requires topological constraints on the implicit function. 5.3.1 Interacting Snakes and Energy Functional Active contours have been successfully used to impose high-level geometrical constraints on low-level features that are extracted from images. Active contours are iteratively deformed based on the initial configuration of the contours and the energy functional that is to be minimized. The initial configuration of interacting snakes is obtained from the coarsely-aligned semantic face graph, and is shown in Fig. 5.8(c). Currently, there are eight snakes interacting with each other. These snakes describe the hair outline, face outline, eyebrows, eyes, nose, and mouth of a face; they are denoted as V (s) = ⋃ N j=1 {v i(s)}, where N (= 8) is the number of snakes, and v i (s) is the i th snake with the parameter s ∈ [0, 1]. The energies used for minimization include the internal energy of a contour (i.e., smoothness and stiffness energies), and the external energy (i.e., the inverse of edge 120
(a) (b) (c) (d) Figure 5.7. Coarse alignment: (a) input face images of size 640 × 480 from the MPEG7 content set (first three rows), and of size 256 × 384 from the MSU database (the fourth row); (b) detected faces; (c) locations of eyebrow, nostril, and mouth lines using shadow maps; (d) face images overlaid with coarsely aligned face graphs. 121
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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
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
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
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
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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
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Page 155 and 156: (a) (b) (c) (d) Figure 5.18. Exampl
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Page 159 and 160: (a) (b) (c) (d) (e) (f) (g) Figure
Page 161 and 162: 5.6 Summary For overcoming variatio
Page 163 and 164: such vision-based systems are (i) d
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
Page 177 and 178: adius of a cluster ‘i’ w.r.t. a
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
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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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