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

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for topographic facial features (e.g., eyebrows, cheek, mouth, etc.) Mean MEF1 MEF2 MEF3 MEF4 MEF5 MEF6 MEF7 MEF8 (a) Mean MDF1 MDF2 MDF3 MDF4 MDF5 MDF6 MDF7 MDF8 (b) Figure 2.3. Internal representations of the PCA-based approach and the LDA-based approach (from Weng and Swets [31]). The average (mean) images are shown in the first column. Most Expressive Features (MEF) and Most Discriminating Features (MDF) are shown in (a) and (b), respectively. The PCA-based algorithm provides a compact but non-local representation of face images. Based on the appearance of an image at a specific view, the PCA algorithm works at the pixel level. Hence, the algorithm can be regarded as “picture” recognition, in other words, it is not explicitly using any facial features. The EBGMbased algorithm constructs local features (extracted using Gabor wavelets) and global face shape (represented as a graph), and so this approach is much closer to “face” recognition. However, the EBGM algorithm is pose-dependent, and it requires initial graphs for different poses during its training stage. The LFA-based algorithm is derived from the PCA-based method; it is also called a kernel PCA method. In this approach, however, the choice of kernel functions for local facial features (e.g., eyes, mouth, and nose) and the selection of locations of these features still remains an open 42
(a) (b) (c) (d) Figure 2.4. Internal representations of the EBGM-based approach (from Wiskott et al. [32]): (a) a graph is overlaid on a face image; (b) a reconstruction of the image from the graph; (c) a reconstruction of the image from a face bunch graph using the best fitting jet at each node. Images are downloaded from [33]; (d) a bunch graph whose nodes are associated with a bunch of jets [33]; (e) an alternative interpretation of the concept of a bunch graph [33]. (e) question. In addition to these three approaches, we categorize face recognition algorithms on the basis of pose-dependency and matching features (see Fig. 2.6). In pose-dependent algorithms, a face is represented by a set of viewer-centered images. A small number of 2D images (appearances) of a human face at different poses are stored as a representative set of the face, while the 3D face shape is implicitly represented in the set. 43
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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
Page 9 and 10: 4 Face Modeling 97 4.1 Modeling Met
Page 11 and 12: List of Figures 1.1 Applications us
Page 13 and 14: 2.6 A breakdown of face recognition
Page 15 and 16: 4.2 3D triangular-mesh model and it
Page 17 and 18: 5.14 Fine alignment using geodesic
Page 19 and 20: Chapter 1 Introduction In recent ye
Page 21 and 22: (a) (b) Figure 1.1. Applications us
Page 23 and 24: (e) Figure 1.1. (Cont’d). (f) (a)
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Page 29 and 30: the motivation for studies that att
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Page 37 and 38: algorithm can also provide geometri
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Page 41 and 42: 1.5.1 Face Alignment Using 2.5D Sna
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Page 45 and 46: Using merely low-level features (e.
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Page 51 and 52: Chapter 2 Literature Review We firs
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Page 83 and 84: (a) (b) Figure 3.3. The Y C b C r c
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Page 87 and 88: 3.3 Localization of Facial Features
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Page 95 and 96: Figure 3.12. Computation of face bo
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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
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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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Face Detection and Modeling for Recognition - Biometrics Research ...

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