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

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similar configuration of facial components, the biased average facial topology of the generic face model, and coarse head pose. Figures 5.19, 5.20, and 5.21 show the reconstructed semantic face graphs, G P in Eq. (5.23), (compare them with G 0 in Fig. 5.1(c)) at three levels of details, respectively. Each coarse alignment and fine (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) Figure 5.19. Cartoon faces reconstructed from Fourier descriptors using all the frequency components: (a) to (j) are ten average cartoon faces for ten different subjects based on five images for each subject. Individual components are shown in different shades in (a) to (e). alignment on an image of size 640 × 480 takes 10 sec with C implementation and 460 sec. with MATLAB implementation, repsectively, while each face comparison takes 0.0029 sec with Matlab implementation on a 1.7 GHz CPU. We are conducting other cross-validation tests for classification, and are in the process of performing recognition on gallery (containing known subjects) and probe (containing unknown subject) databases. Although the alignment is off-line currently, there is large room to enhance the performance of alignment implementation to make it operate in real- 138
(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) Figure 5.20. Cartoon faces reconstructed from Fourier descriptors using only 50% of the frequency components: (a) to (j) are ten average cartoon faces for ten different subjects based on five images for each subject. Individual components are shown in different shades in (a) to (e). (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) Figure 5.21. Cartoon faces reconstructed from Fourier descriptors using only 30% of the frequency components: (a) to (j) are ten average cartoon faces for ten different subjects based on five images for each subject. Individual components are shown in different shades in (a) to (e). 139
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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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mouth is performed within the face
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Figure 3.12. Computation of face bo
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center, and lengths of major and mi
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segment. The other term favors an u
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(a) (b) (c) (d) (e) Figure 3.15. Fa
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(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
Page 137 and 138: (a) (b) (c) (d) Figure 5.5. Boundar
Page 139 and 140: (a) (b) (c) (d) Figure 5.7. Coarse
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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 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
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
Page 195 and 196: [141] M.D. Adams and F. Kossentini,
Page 197 and 198: [169] P.T. Jackway and M. Deriche,
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Face Detection and Modeling for Recognition - Biometrics Research ...

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