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

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Figure 3.1. Face detection algorithm. The face localization module finds face candidates, which are verified by the detection module based on facial features. 60
oundary. A face score is computed for each set of eyes, mouth and the ellipse. Figure 3.1 shows a detected face and the enclosing ellipse with its associated eyemouth triangle which has the highest score that exceeds a threshold. These detected facial features are grouped into a structured facial descriptor in the form of a 2D graph for face description. These descriptors can be the input to subsequent modules such as face modeling and recognition. We now describe in detail the individual components of the face detection algorithm. 3.2 Lighting Compensation and Skin Tone Detection The appearance of the skin-tone color can change due to different lighting conditions. We introduce a lighting compensation technique that uses “reference white” to normalize the color appearance. We regard pixels with the top 5% of the luma (nonlinear gamma-corrected luminance) values as the reference white if the number of these pixels is sufficiently large (> 100). The red, green, and blue components of a color image are adjusted so that these reference-white pixels are scaled to the gray level of 255. The color components are unaltered if a sufficient number of reference-white pixels is not detected. This assumption is reasonable not only because an image usually contains “real white” (i.e., white reference in [155]) pixels in some regions of interest (such as eye regions), but also because the dominant bias color always appears in the “real white”. Figure 3.2 demonstrates an example of our lighting compensation 61
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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
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
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: size) to generate potential face ca
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
Page 93 and 94: mouth is performed within the face
Page 95 and 96: Figure 3.12. Computation of face bo
Page 97 and 98: center, and lengths of major and mi
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 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
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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
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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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