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

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Table 3.1 Detection results on the HHI image database (Image size 640 × 480) on a PC with 1.7 GHz CPU. FP: False Positives, DR: Detection Rate. Head Pose Frontal Near- Half- Profile Total Frontal Profile No. of images 66 54 75 11 206 Stage 1: Grouped skin regions No. of FP 3145 2203 3781 277 9406 DR (%) 95.45 98.15 96.00 100 96.60 Time (sec): average ± s. d. 1.56 ± 0.45 Stage 2: Rectangle merge No. of FP 468 287 582 39 1376 DR (%) 95.45 98.15 96.00 100 96.60 Time (sec): average ± s. d. 0.18 ± 0.23 Stage 3: Facial feature detection No. of FP 4 6 14 3 27 DR (%) 89.40 90.74 74.67 18.18 80.58 Time (sec): average ± s. d. 22.97 ± 17.35 Table 3.2 Detection results on the Champion database (Image size ∼ 150 × 220) on a PC with 860 MHz CPU. FP: False Positives, DR: Detection Rate. Stage 1 2 3 No. of images 227 No. of FP 5582 382 14 DR (%) 99.12 99.12 91.63 Time (sec): average ± s. d. 0.080 ± 0.036 0.012 ± 0.020 5.780 ± 4.980 88
(a) (b) (c) (d) Figure 3.20. Face detection results on a subset of the HHI database: (a) input images; (b) grouped skin regions; (c) face candidates; (d) detected faces are overlaid on the lighting-compensated images. 89
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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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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
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Page 95 and 96: Figure 3.12. Computation of face bo
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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: The number of false positives is al
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
Page 141 and 142: In the Baysian framework, given an
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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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(a) (b) (c) (d) (e) (f) (g) (h) (i)
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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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