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

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different representations lead to different matching algorithms. We can design a database management system that utilizes the outputs of our face detection and modeling modules as indices to search a database based on the semantic concepts, such as “find all the images containing John’s faces” and “search faces which have Vincent’s eyes (or face shape). 1.7 Outline of Dissertation This dissertation is organized as follows. Chapter 2 presents a brief literature review on face detection and recognition, face modeling (including model compression), and face retrieval. In Chapter 3, we present our face detection algorithm for color images. Chapter 4 discusses our range data-based face modeling method for recognition. Chapter 5 describes the semantic face recognition system, including face alignment using interacting snakes, a semantic face matching algorithm, and the generation of cartoon faces and facial caricatures. Chapter 6 presents conclusions and future directions related to this work. 1.8 Dissertation Contributions The major contributions of this dissertation are categorized into the topics of face detection, face modeling, and face recognition. In face detection, we have developed a new face detection algorithm for multiple non-profile-view faces with complex background in color images, based on localization of skin-tone color and facial features 28
such as eyes, mouth and face boundary. The main properties of this algorithms are listed as follows. • Lighting compensation: This method corrects the color bias and recovers the skin-tone color by automatically estimating the reference white pixels in a color image, under the assumption that an image usually contains “real white” (i.e., white reference) pixels and the dominant bias color in an image always appears as “real white”. • Non-linear color transformation: In literature, the chrominance components of the skin tone have been assumed to be independent of the luminance component of the skin tone. We found that the chroma of skin tone depends on the luma. We overcome the difficulty of detecting the low-luma and high-luma skin tone colors by applying a nonlinear transform to the Y C b C r color space. The transformation is based on the linearly fitted boundaries of our training skin cluster in Y C b and Y C r color subspaces. • Modeling a skin-tone color classifier as an elliptical region: A simple classifier which constructs an elliptical decision region in the chroma subspace, C b C r , has been designed, under the assumption of the Gaussian distribution of skin tone color. • Construction of facial feature maps for eyes, mouth, and face boundary: With the use of gray-scale morphological operators (dilation and erosion), we construct these feature maps by integrating the luminance and chrominance information of facial features. For example, eye regions have high C b (difference 29
Page 1 and 2: Face Detection and Modeling for Rec
Page 3 and 4: perimental results demonstrate succ
Page 5 and 6: To my parents; my lovely wife, Pei-
Page 7 and 8: 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)
Page 25 and 26: esolutions and of poor quality (i.e
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: Using merely low-level features (e.
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
Page 57 and 58: esult in different recognition appr
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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
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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center, and lengths of major and mi
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segment. The other term favors an u
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(a) (b) (c) (d) (e) Figure 3.15. Fa
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(a) (b) (c) (d) (e) Figure 3.18. Fa
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The number of false positives is al
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(a) (b) (c) (d) Figure 3.20. Face d
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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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