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

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Figure 4.1. The system overview of the proposed modeling method based on a 3D generic face model. disparity maps) obtained for an individual. The generic face model is modified so that these recognition-oriented features are fitted to the individual’s facial geometry. The modeling process aligns and adapts the generic face model to the facial measurements in a global-to-local fashion. The overview of our face modeling method is given in Fig. 4.1. The input to the modeling algorithm is the generic face model and the facial measurements. The modeling method contains two major modules: (i) global alignment and (ii) local adaptation. The global alignment module changes the size of the generic face model, and aligns the scaled generic model according to the 3D head pose. The local adaptation module refines the facial features of the globally aligned generic face model iteratively and locally. We do not extract isosurfaces directly from facial measurements because facial measurements are often noisy (e.g., near the ears 98
and nose in frontal views), and because the extraction is time-consuming and usually generates triangles of the same size in the mesh. Hence, in our model construction, the desired recognition-oriented facial features can be specified and gradually modified in the 3D generic face model. The modeling algorithm generates an adapted/learned 3D face model with aligned facial texture. The 2D projections of the texture-mapped 3D model are further used for face verification and recognition. 4.2 Generic Face Model We choose Waters’ animation model [69], which contains 256 vertices and 441 facets for one half of the face, because this model captures most of the facial features that are needed for face recognition (as well as animation), and because triangular meshes are suitable for free-form surfaces like faces [136]. Figure 4.2 shows the frontal and one side view of the model, and facial features such as eyes, nose, mouth, face boundary, and chin. There are openings at both the eyes and the mouth, which can be manipulated. The Phong-shaded appearance of this model is shown for three different views in Fig. 4.3. 99
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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
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(e) Figure 1.1. (Cont’d). (f) (a)
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esolutions and of poor quality (i.e
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can recognize known faces in carica
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the motivation for studies that att
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(a) Figure 1.10. Similarity of fron
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verify the face present in an image
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17 Figure 1.12. System diagram of o
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algorithm can also provide geometri
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commercial parametric face modeling
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1.5.1 Face Alignment Using 2.5D Sna
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1.5.3 Face Alignment Using Interact
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Using merely low-level features (e.
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such as eyes, mouth and face bounda
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can be applied to a 3D face model w
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Chapter 2 Literature Review We firs
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mation theory, geometrical modeling
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(e) (f) (g) (h) (i) (j) (k) (l) Fig
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esult in different recognition appr
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(a) (b) (c) Figure 2.2. Examples of
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(a) (b) (c) (d) Figure 2.4. Interna
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Figure 2.6. A breakdown of face rec
Page 65 and 66: 2.3 Face Modeling Face modeling pla
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Page 73 and 74: low-level features can provide mean
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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
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Page 87 and 88: 3.3 Localization of Facial Features
Page 89 and 90: (a) (b) (c) Figure 3.9. Constructio
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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 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: Chapter 4 Face Modeling We first in
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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Page 129 and 130: Chapter 5 Semantic Face Recognition
Page 131 and 132: (a) (b) (c) Figure 5.1. Semantic fa
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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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Face Detection and Modeling for Recognition - Biometrics Research ...

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