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

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(a) Figure 2.5. Internal representations of the LFA-based approach (from Penev and Atick [34]). (a) An average face image is marked with five localized features; (b) five topographic kernels associated with the five localized features are shown in the top row, and the corresponding residual correlations are shown in the bottom row. (b) The representative set can be obtained from either digital cameras or extracted from videos. On the other hand, in pose-invariant approaches, a face is represented by a 3D face model. The 3D face shape of an individual is explicitly represented, while the 2D images are implicitly encoded in this face model. The 3D face models can be constructed by using either 3D digitizers or range sensors, or by modifying a generic face model using a video sequence or still face images of frontal and profile views. The pose-dependent algorithms can be further divided into three classes: appearance-based (holistic) [29], [78] feature-based (analytic) [102], [103] and hybrid (which combines holistic and analytic methods) [60], [99], [32], [34] approaches. The appearance-based methods are sensitive to intra-subject variations, especially to changes in hairstyle, because they are based on global information in an image. However, the feature-based methods suffer from the difficulty of detecting local fiducial “points”. The hybrid approaches were proposed to accommodate both global and local face shape information. For example, LFA-based methods, eigen-template meth- 44
Figure 2.6. A breakdown of face recognition algorithms based on the posedependency, face representation, and features used in matching. ods, and shape-and-shape-free [104] methods belong to the hybrid approach which is derived from the PCA methodology. The EBGM-based methods belong to the hybrid approach that is based on 2D face graphs and wavelet transforms at each feature node of the graphs. Although they are in the hybrid approach category, the eigen-template matching and EBGM-based methods are much closer to feature-based approaches. In the pose-invariant algorithms, 3D face models are utilized to reduce the variations in pose and illumination. Gordon et al. [105] proposed an identification system based on 3D face recognition. The 3D model used by Gordon et al. is represented by a number of 3D points associated with their corresponding texture features. This method requires an accurate estimate of the face pose. Lengagne et al. [106] proposed a 3D face reconstruction scheme using a pair of stereo images for recognition and mod- 45
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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
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
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
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Page 65 and 66: 2.3 Face Modeling Face modeling pla
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Page 71 and 72: are listed in Table 2.2. All of the
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Page 77 and 78: size) to generate potential face ca
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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
Page 85 and 86: (a) Figure 3.5. 2D projections of t
Page 87 and 88: 3.3 Localization of Facial Features
Page 89 and 90: (a) (b) (c) Figure 3.9. Constructio
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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Page 105 and 106: The number of false positives is al
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Page 109 and 110: Figure 3.22. Face detection results
Page 111 and 112: 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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