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

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• A face tracking and recognition prototype: A prototype of a recognition system with tracking modules is shown in Fig. 6.1. The detection and tracking Figure 6.1. A prototype of a face identification system with the tracking function. modules include (i) a motion filter if temporal information is available, (ii) a human body detector and analysis of human gait, and (iii) a motion predictor of face and the human body. • Preliminary tracking results: An example of detection of motion and skin color is shown in Figure 6.2 (see [185] for more details). The preliminary results of off-line face tracking based on the detection of interframe difference, skin color, and facial features are shown in Fig. 6.3, which contains a sequence of 25 video frames. These images are lighting-compensated and overlaid with detected faces. The image sequence shows two subjects entering the PRIP Lab 148
(a) (b) (c) (d) Figure 6.2. An example of motion detection in a video frame: (a) A color video frame; (b) extracted regions with significant motion; (c) detected moving skin patches shown in pseudocolor; (d) extracted face candidates described by rectangles. in the Engineering building at Michigan State University through two different doors. Faces of different sizes and poses are successfully tracked under various lighting conditions. 6.2.2 Face Modeling We have developed two face modeling methods for range (with registered color) data and for color data in a frontal view. Once we construct a pose estimator, we can modify the proposed methods of face alignment for non-frontal views. This extension of modeling includes the following tasks: • Complete head and ear mesh models: We need model polygons for hair/head portion and ears in order to generate hair and ear outlines in nonfrontal views. • Pose and illumination estimation: We can design a head pose estimator and an illumination estimator for faces in frontal and non-frontal views based on the locations of face and facial components, and shadows/shadings on the face. 149
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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
Page 115 and 116: Chapter 4 Face Modeling We first in
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Page 119 and 120: 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 161 and 162: 5.6 Summary For overcoming variatio
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Page 169 and 170: • Non-frontal training views: Acc
Page 171 and 172: Appendices
Page 173 and 174: ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ Y
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Page 179 and 180: Appendix C Image Processing Templat
Page 181 and 182: considering pixel classes as voxel
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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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