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

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Table of Contents LIST OF TABLES LIST OF FIGURES x xi 1 Introduction 1 1.1 Challenges in Face Recognition . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Semantic Facial Components . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3 Face Recognition Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4 Face Detection and Recognition . . . . . . . . . . . . . . . . . . . . . . . 15 1.5 Face Modeling for Recognition . . . . . . . . . . . . . . . . . . . . . . . . 19 1.5.1 Face Alignment Using 2.5D Snakes . . . . . . . . . . . . . . . . . . . . 23 1.5.2 Model Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.5.3 Face Alignment Using Interacting Snakes . . . . . . . . . . . . . . . . . 25 1.6 Face Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.7 Outline of Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 1.8 Dissertation Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2 Literature Review 33 2.1 Face Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2 Face Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2.3 Face Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.3.1 Generic Face Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.3.2 Snakes for Face Alignment . . . . . . . . . . . . . . . . . . . . . . . . . 50 2.3.3 3D Model Compression . . . . . . . . . . . . . . . . . . . . . . . . . . 52 2.4 Face Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3 Face Detection 58 3.1 Face Detection Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.2 Lighting Compensation and Skin Tone Detection . . . . . . . . . . . . . 61 3.3 Localization of Facial Features . . . . . . . . . . . . . . . . . . . . . . . . 69 3.3.1 Eye Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 3.3.2 Mouth Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.3.3 Eye and Mouth Candidates . . . . . . . . . . . . . . . . . . . . . . . . 74 3.3.4 Face Boundary Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 3.3.5 Weight Selection for a Face Candidate . . . . . . . . . . . . . . . . . . 79 3.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 viii
4 Face Modeling 97 4.1 Modeling Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 4.2 Generic Face Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4.3 Facial Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 4.4 Model Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 5 Semantic Face Recognition 111 5.1 Semantic Face Graph as Multiple Snakes . . . . . . . . . . . . . . . . . . 112 5.2 Coarse Alignment of Semantic Face Graph . . . . . . . . . . . . . . . . . 115 5.3 Fine Alignment of Semantic Face Graph via Interacting Snakes . . . . . 118 5.3.1 Interacting Snakes and Energy Functional . . . . . . . . . . . . . . . . 120 5.3.2 Parametric Active Contours . . . . . . . . . . . . . . . . . . . . . . . . 127 5.3.3 Geodesic Active Contours . . . . . . . . . . . . . . . . . . . . . . . . . 127 5.4 Semantic Face Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 5.4.1 Component Weights and Matching Cost . . . . . . . . . . . . . . . . . 132 5.4.2 Face Matching Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 133 5.4.3 Face Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 5.5 Facial Caricatures for Recognition and Visualization . . . . . . . . . . . 140 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 6 Conclusions and Future Directions 144 6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 6.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 6.2.1 Face Detection & Tracking . . . . . . . . . . . . . . . . . . . . . . . . . 147 6.2.2 Face Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 6.2.3 Face matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 APPENDICES 153 A Transformation of Color Space 153 A.1 Linear Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 A.2 Nonlinear Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . 154 A.3 Skin Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 B Distance between Skin Patches 157 C Image Processing Template Library (IPTL) 160 C.1 Image and Image Template . . . . . . . . . . . . . . . . . . . . . . . . . 160 C.2 Example Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 BIBLIOGRAPHY 165 ix
Page 1 and 2: Face Detection and Modeling for Rec
Page 3 and 4: perimental results demonstrate succ
Page 5 and 6: To my parents; my lovely wife, Pei-
Page 7: for providing the range datasets; t
Page 11 and 12: List of Figures 1.1 Applications us
Page 13 and 14: 2.6 A breakdown of face recognition
Page 15 and 16: 4.2 3D triangular-mesh model and it
Page 17 and 18: 5.14 Fine alignment using geodesic
Page 19 and 20: Chapter 1 Introduction In recent ye
Page 21 and 22: (a) (b) Figure 1.1. Applications us
Page 23 and 24: (e) Figure 1.1. (Cont’d). (f) (a)
Page 25 and 26: esolutions and of poor quality (i.e
Page 27 and 28: can recognize known faces in carica
Page 29 and 30: the motivation for studies that att
Page 31 and 32: (a) Figure 1.10. Similarity of fron
Page 33 and 34: verify the face present in an image
Page 35 and 36: 17 Figure 1.12. System diagram of o
Page 37 and 38: algorithm can also provide geometri
Page 39 and 40: commercial parametric face modeling
Page 41 and 42: 1.5.1 Face Alignment Using 2.5D Sna
Page 43 and 44: 1.5.3 Face Alignment Using Interact
Page 45 and 46: Using merely low-level features (e.
Page 47 and 48: such as eyes, mouth and face bounda
Page 49 and 50: can be applied to a 3D face model w
Page 51 and 52: Chapter 2 Literature Review We firs
Page 53 and 54: mation theory, geometrical modeling
Page 55 and 56: (e) (f) (g) (h) (i) (j) (k) (l) Fig
Page 57 and 58: 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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and nose in frontal views), and bec
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4.3 Facial Measurements Facial meas
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4.4 Model Construction Our face mod
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a feature-dependent decay factor. F
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is to find appropriate external ene
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(a) (b) (d) (e) (f) Figure 4.11. Te
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Chapter 5 Semantic Face Recognition
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(a) (b) (c) Figure 5.1. Semantic fa
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of Fourier series truncation. In ad
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ponent color. The semantic facial s
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(a) (b) (c) (d) Figure 5.5. Boundar
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(a) (b) (c) (d) Figure 5.7. Coarse
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In the Baysian framework, given an
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edge map; hence it requires a clean
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shows examples of mouth energies. F
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[ ( ∂Φ ∂t =∇Φ µ 1 div(g
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(a) (b) (c) (d) (e) (f) Figure 5.14
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y the reliability of component matc
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tation, face size, and lighting con
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(a) (b) (c) (d) Figure 5.18. Exampl
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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
Page 191 and 192:
[84] M. Abdel-Mottaleb and A. Elgam
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[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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