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

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Table 2.3 Summary of performance of various face detection approaches. Authors Year Head Pose Féraud et al. [19] Maio et al. [20] Schneiderman et al. [22] Garcia et al. [21] Rowley et al. [24], [23] Yow et al. [26] 2001 Frontal to profile Test Databases Sussex; CMU test1; Web images Detection Rate 100% for Sussex; 81% ∼ 86% for CMU test1; 74.7% ∼ 80.1% for Web images. 2000 Frontal Static images 89.53% ∼ 91.34% 2000 Frontal to profile 1999 Frontal to near frontal 1998 (Upright) frontal 1997 Frontal to profile CMU; images Web MPEG videos 93.27% CMU; FERET; Web images 75.24% ∼ 92.7% CMU 84% ∼ 92% Lew et al. [27] 1996 Frontal MIT; CMU; Leiden 86%[24]; 79.6%[23] for rotated faces 87% ∼ 95% the appearance-based (holistic) approach has greatly advanced the field of face recognition. By categorizing face detection methods based on their representations of the face, we observe that detection/recognition algorithms using holistic representations have the advantage of finding/identifying small faces or faces in poor-quality images (i.e. detection/recognition under uncertainty), while those using geometrical facial features provide a good solution for detecting/recognizing faces in different poses and expressions. The internal representation of a human face substantially affects the performance and design of a detection or recognition system. A seamless combination of 56
holistic 2D and geometrical 3D features provides a promising approach to represent faces for face detection as well as face recognition. Modeling human face in 3D space has been shown to be useful for face recognition. However, the important aspect of face modeling is how to efficiently encode the 3D facial geometry and texture as compact features for face recognition. 57
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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
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.
Page 47 and 48: such as eyes, mouth and face bounda
Page 49 and 50: can be applied to a 3D face model w
Page 51 and 52: Chapter 2 Literature Review We firs
Page 53 and 54: mation theory, geometrical modeling
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Page 57 and 58: esult in different recognition appr
Page 59 and 60: (a) (b) (c) Figure 2.2. Examples of
Page 61 and 62: (a) (b) (c) (d) Figure 2.4. Interna
Page 63 and 64: Figure 2.6. A breakdown of face rec
Page 65 and 66: 2.3 Face Modeling Face modeling pla
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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
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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
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
Page 103 and 104: (a) (b) (c) (d) (e) Figure 3.18. Fa
Page 105 and 106: The number of false positives is al
Page 107 and 108: (a) (b) (c) (d) Figure 3.20. Face d
Page 109 and 110: Figure 3.22. Face detection results
Page 111 and 112: Figure 3.22. (Cont’d). 93
Page 113 and 114: 3.5 Summary We have presented a fac
Page 115 and 116: Chapter 4 Face Modeling We first in
Page 117 and 118: and nose in frontal views), and bec
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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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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