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

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(a) (b) (c) (d) (e) Figure 4.4. Facial measurements of a human face: (a) color image; (b) range map; and the range map with texture mapped for (c) a left view; (d) a profile view; (e) a right view. (a) (b) Figure 4.5. Facial features overlaid on the color image, (a) obtained from face detection; (b) generated for face modeling. 102
4.4 Model Construction Our face modeling process consists of global alignment and local adaptation. Global alignment first brings the generic model and facial measurements into the same coordinate system. Based on the 3D head pose and the face size, the generic model is then scaled, rotated, and translated to fit the facial measurements. Figure 4.6 shows the global alignment results in two different modes. Local adaptation consists of local (a) Figure 4.6. Global alignment of the generic model (in red) to the facial measurements (in blue): the target mesh is plotted in (a) for a hidden line removal mode for a side view; (b) for a see-through mode for a profile view. (b) alignment and local feature refinement. Local alignment involves scaling and translating model features, such as eyes, nose, mouth, chin and face boundary to fit the extracted facial features. Local feature refinement makes use of two new techniques– displacement propagation and 2.5D active contours–to smooth the face model and to refine local features. The local alignment and the local refinement of each feature 103
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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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Page 71 and 72: are listed in Table 2.2. All of the
Page 73 and 74: low-level features can provide mean
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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
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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
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: 4.3 Facial Measurements Facial meas
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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 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 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
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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
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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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