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

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Appendix A Transformation of Color Space In this appendix, we will give the detailed formulae of two types of colorspace transformations and an elliptical skins classifier, which are used in our face detection algorithm. The transformations include a linear transformation between RGB and Y CbCr color spaces and a nonlinear transformation applied to Y CbCr for compensating the luma dependency. The skin classifier is described by an elliptical region, which lies in the nonlinearly transformed Y CbCr space. A.1 Linear Transformation Our face detection algorithm utilizes a linear transformation to convert the color components of an input image in the RGB space into those in the Y CbCr space for separating the luma component from chroma components of the input image. The transformation between these two space is formulated in Eqs. (A.1) and (A.2) for the value of color components that range from 0 to 255 (see the details in [155]). 153
⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ Y ⎣Cb⎦ = 1 65 129.057 25.064 R 16 ⎣−37.945 −74.494 112.439 ⎦ ⎣G⎦ + ⎣128⎦(A.1) 256 Cr 112.439 −94.154 −18.285 B 128 ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ R ⎣G⎦ = 1 298.082 0 408.583 Y ⎣298.082 −100.291 −208.120⎦ ⎣Cb⎦ (A.2) 256 B 298.082 516.411 0 Cr Figures A.1 (a) and (b) illustrate a set of randomly sampled reproducible colors in the RGB space and its corresponding set in the Y CbCr space. (a) Figure A.1. (b) Color spaces: (a) RGB; (b) Y CbCr. A.2 Nonlinear Transformation In the Y C b C r color space, we can regard the chroma (C b and C r ) as functions of the luma (Y ): C b (Y ) and C r (Y ). Let the transformed chroma be C b ′(Y ) and C′ r(Y ). The nonlinear transformation converts the elongated cluster into a cylinder-like shape, 154
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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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and nose in frontal views), and bec
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4.3 Facial Measurements Facial meas
Page 121 and 122: 4.4 Model Construction Our face mod
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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
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Page 155 and 156: (a) (b) (c) (d) Figure 5.18. Exampl
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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
Page 169 and 170: • Non-frontal training views: Acc
Page 171: Appendices
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
Page 183 and 184: gray8imageA(120,120) = 200; // Asse
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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