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

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150 Figure 6.3. Face tracking results on a sequence of 25 video frames. These images are arranged from top to bottom and from left to right. Detected faces are overlaid on the lighting-compensated images.
• Non-frontal training views: According to the estimated head pose, we can rotate the generic face model and generate the boundary curves of the semantic components for face alignment at the estimated pose. 6.2.3 Face matching We have designed a semantic face matching algorithm based on the component weights derived from distinctiveness and visibility of individual facial components. Currently, the semantic graph descriptors, SGD i in Section 5.4.1, used for comparing the difference between facial components contain only the shape information (i.e., component contours). We can improve the performance of the algorithm by including the following properties: • Texture information: Associate a semantic graph descriptor with a set of texture information (e.g., wavelet coefficients, photometric sketches [55], and normalized color values) for each facial component. The semantic face matching algorithm will compare faces based on both the shape and texture information. • Scalability: Evaluate the matching algorithm on several public domain face databases. • Caricature effects on recognition: Explore other weighting functions on the distinctiveness of individual facial components based on the visualized facial caricature and the recognition performance. • Facial statistics: Analyze face shape, race, sex, and age, and construct other 151
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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
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The number of false positives is al
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(a) (b) (c) (d) Figure 3.20. Face d
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Figure 3.22. Face detection results
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Figure 3.22. (Cont’d). 93
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3.5 Summary We have presented a fac
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Chapter 4 Face Modeling We first in
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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 149 and 150: (a) (b) (c) (d) (e) (f) Figure 5.14
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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: (a) (b) (c) (d) Figure 6.2. An exam
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
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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