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

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C.2 Example Code Example code of the user interface for the pixel classes and the image template is given below. #include ”iptl.imagechnl.h” int main(){ // Conversion of pixel types // GREY8 graypix = 10; FLOAT32 flpix = 3.5; ColorRGB colorpix(42,53,64); colorpix = graypix; // Assign a gray value graypix = flpix; // Truncate data colorpix.r = 100; // Change the red component graypix = colorpix; // Compute luminance flpix += 20.7; // Arithmetic operations // Image manipulation // // Image Creation ImageT gray8imageA(HOSTRAM, 128, 128, GREY8(55)); ImageT gray8imageB(HOSTRAM, 128, 128, GREY8(100)); ImageT gray16imageC; // An empty image //Creation of color images //Data arrangement of the image is RGB... RGB... ImageT rgbimage(HOSTRAM,32,32,128); //Data arrangement of the image is RRR...GGG...BBB... ImageRGB rgbimagechnl(rgbimage1); // A template function for converting images from one type to another // gray8imageA = rgbimage; // Extract Luminance gray16imageC = gray8imageB; // Enlarge the dynamic range of gray values gray8imageB -= gray8imageA; // Image subtraction gray8imageA[5] = 100; // Assess pixels as a 1D vector 163
gray8imageA(120,120) = 200; // Assess pixels as a 2D image } // end of main() We refer the reader to the IPTL reference manual for the details of template implementation. 164
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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
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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
Page 131 and 132: (a) (b) (c) Figure 5.1. Semantic fa
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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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Page 155 and 156: (a) (b) (c) (d) Figure 5.18. Exampl
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Page 159 and 160: (a) (b) (c) (d) (e) (f) (g) Figure
Page 161 and 162: 5.6 Summary For overcoming variatio
Page 163 and 164: such vision-based systems are (i) d
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 and 172: Appendices
Page 173 and 174: ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ Y
Page 175 and 176: and are shown as blue-dashed lines
Page 177 and 178: adius of a cluster ‘i’ w.r.t. a
Page 179 and 180: Appendix C Image Processing Templat
Page 181: considering pixel classes as voxel
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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