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

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(a) (b) Figure 2.7. Face modeling using anthropometric measurements (downloaded from [35]): (a) anthropometric measurements; (b) a B-spline face model. be used for face modeling. For instance, Lengagne et al. [106] and Chen et al. [112] built face models from a pair of stereo images. Atick et al. [107] and Yan et al. [108] reconstructed 3D face surfaces based on the Karhonen-Loeve (KL) transform and the shape-from-shading technique. Zhao et al. [109] made use of a symmetric shape-from-shading technique to build a 3D face model for recognition. There are other methods which combine both shape-from-stereo (which extracts low-spatial frequency components of 3D shape) and shape-from-shading (extracting high-spatial frequency components) to reconstruct 3D faces [113], [114], [115]. See [116] for additional methods to obtain facial surface data. However, currently it is still difficult to extract sufficient information about the facial geometry only from 2D images. This difficulty is the reason why Guenter et al. [117] utilize a large number of fiducial points to capture 3D face geometry for photorealistic animation. Even though we can obtain dense 3D facial measurements from high-cost 3D digitizers, it takes too much time and it is expensive to scan a large number of human subjects. 48
An advanced modeling approach which incorporates a priori knowledge of facial geometry has been proposed for efficiently building face models. We call the model representing the general facial geometry as a generic face model. Waters’ face model [69], shown in Fig. 2.8(a), is a well-known instance of polygonal facial surfaces. Figure 2.8(b) shows some other generic face models. The one used by Blanz and Vet- Y 6 4 2 0 −2 −4 −6 −8 −4 −2 0 2 4 X (a) Figure 2.8. Generic face models: (a) Water’s animation model; (b) anthropometric measurements; (b) six kinds of face models for representing general facial geometry. (b) ter is a statistics-based face model which is represented by the principal components of shape and texture data. Reinders et al. [72] used a fairly coarse wire-frame model, compared to Waters’ model, to do model adaptation for image coding. Yin et al. [118] proposed a MPEG4 face modeling method that uses fiducial points extracted from two face images at frontal and profile views. Their feature extraction is simply based on the results of intensity thresholding and edge detection. Similarly, Lee et al. [119] have proposed a method that modifies a generic model using either two orthogonal pictures (frontal and profile views) or range data, for animation. Similarly, 49
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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
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.
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
Page 55 and 56: (e) (f) (g) (h) (i) (j) (k) (l) Fig
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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: 2.3 Face Modeling Face modeling pla
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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
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
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Page 107 and 108: (a) (b) (c) (d) Figure 3.20. Face d
Page 109 and 110: Figure 3.22. Face detection results
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Page 113 and 114: 3.5 Summary We have presented a fac
Page 115 and 116: 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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