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

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facial hair, lighting, and aging). Some variations are not removable, while others can be compensated for recognition. Persons who have similar face appearances, e.g. twins, and an individual who could have different appearances due to cosmetics, or other changes in facial hair and glasses are very difficult to recognize. Variations due to different poses, illuminations, and facial expressions are relatively easy to handle. Currently available algorithms for face recognition concentrate on recognizing faces under those variations which can somehow be compensated for. Because facial variations due to pose cause a large amount of appearance change, more and more systems are taking advantage of 3D face geometry for recognition. The performance of a recognition algorithm depends on the face databases it is evaluated on. Several face databases, such as MIT [95], Yale [96], Purdue [97], and Olivetti [98] databases are publically available for researchers. Figure 2.2 shows some examples of face images from the FERET [28], MIT [29], and XM2VTS [30] databases. According to Phillips [68], [28], the FERET evaluation of face recognition algorithms identifies three state-of-the-art techniques: (i) the principal component analysis (PCA)-based approach [99], [100], [29]; (ii) the elastic bunch graphic matching (EBGM)-based paradigm [32]; and (iii) the local feature analysis (LFA)-based approach [34], [101]. The internal representations of PCA-based, EBGM-based, and LFA-based recognition approaches are shown in Figs. 2.3, 2.4, and 2.5, respectively. To represent and match faces, the PCA-based approach makes use of a set of orthonormal basis images; the EBGM-based approach constructs a face bunch graph, whose nodes are associated with a set of wavelet coefficients (called jets); the LFA-based approach uses localized kernels, which are constructed from PCA-based eigenvectors, 40
(a) (b) (c) Figure 2.2. Examples of face images are selected from (a) the FERET database [28]; (b) the MIT database [29]; (c) the XM2VTS database [30]. 41
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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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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 27 and 28: can recognize known faces in carica
Page 29 and 30: the motivation for studies that att
Page 31 and 32: (a) Figure 1.10. Similarity of fron
Page 33 and 34: verify the face present in an image
Page 35 and 36: 17 Figure 1.12. System diagram of o
Page 37 and 38: algorithm can also provide geometri
Page 39 and 40: commercial parametric face modeling
Page 41 and 42: 1.5.1 Face Alignment Using 2.5D Sna
Page 43 and 44: 1.5.3 Face Alignment Using Interact
Page 45 and 46: Using merely low-level features (e.
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Page 51 and 52: Chapter 2 Literature Review We firs
Page 53 and 54: mation theory, geometrical modeling
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Page 69 and 70: tionals to minimize, implementation
Page 71 and 72: are listed in Table 2.2. All of the
Page 73 and 74: low-level features can provide mean
Page 75 and 76: holistic 2D and geometrical 3D feat
Page 77 and 78: size) to generate potential face ca
Page 79 and 80: oundary. A face score is computed f
Page 81 and 82: normalized red-green (r-g) space [1
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 91 and 92: The eye map from the chroma is enha
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Page 95 and 96: Figure 3.12. Computation of face bo
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Page 101 and 102: (a) (b) (c) (d) (e) Figure 3.15. Fa
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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
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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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