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

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for true-color images, etc. An image is a concrete object; however, Image can be regarded as an abstract data class/type in the field of image processing (IP) and computer vision (CV). Contemporary IP libraries, including the Intel IPL [186], the Intel Open CV [187], and the CVIPTool [188], have designed Image classes using different pixel depths in bits. Our Image Processing Template Library boosts this abstract Image class from a class level to a class template level. The image template, ImageT, is designed based on various pixel classes. For example, pixel classes such as one-bit Boolean, one-byte grayscale, two-byte grayscale, and three-byte true color are the arguments of the image class template. Hence, the conversion between images of different pixel classes is performed at the pixel level, not at the class level. Hence, a large number of algorithms can be reused for images belonging to different pixel classes. Figure C.1 shows the architecture of IPTL class templates. The software architecture can be decomposed into five major levels: platform, base, pixel, image/volume, and movie/space levels. At the platform level, declarations and constants for different working platforms, e.g., the Microsoft Windows and the Sun Unix, are specified in the header file iptl.workingenv.h. At the base level, constants for image processing are defined in the header file iptl.base.h, and space-domain classes for manipulation of different coordinate systems and time-domain classes for evaluation of CPU speed are defined in iptl.geom.h and iptl.time.h, respectively. At the pixel level, the pixel classes such as GRAY8, GRAY16, and ColorRGB24 are defined in iptl.pixel.h. At the image level, the image template ImageT is defined based on its argument of pixel classes. The IPTL is also designed for a volume template, VolumeT, by 161
considering pixel classes as voxel classes. At the movie level, we can derive templates for color images (e.g., ImageRGB and ImageYCbCr) for slices, movies, slides, image display, and image analysis based on the image template. Similarly, based on Figure C.1. Architecture of IPTL class templates. the volume template, we can obtain higher level templates for space, scene, volume display, and volume analysis at the space level. 162
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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
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 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 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: Appendix C Image Processing Templat
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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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