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

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which are shown in the top row in Fig. 4.12. All the 10 test images of the subject shown in the bottom row in Fig. 4.12 were correctly matched to our face model. This preliminary matching experiment shows that the proposed 3D face model is quite useful for recognizing faces at non-frontal views based on the facial appearance. Figure 4.12. Face matching: the top row shows the 15 training images generated from the 3D model; the bottom row shows 10 test images of the subject captured from a CCD camera. 4.5 Summary Face representation plays a crucial role in face recognition systems. For face recognition, we represent a human face as a 3D face model that is learned by adapting a generic 3D face model to input facial measurements in a global-to-local fashion. Based on the facial measurements, our model construction method first aligns the generic model globally, and then aligns and refines each facial feature locally using displacement (of model vertices) propagation and active contours associated with facial features. The final texture mapped model is visually similar to the original face. Initial matching experiments based on the 3D face model show encouraging results for appearance-based recognition. 110
Chapter 5 Semantic Face Recognition In this chapter, we will describe semantic face matching (see Fig. 1.6 in Chapter 1) based on color input images and a generic 3D face model. We will give the details of (i) the face modeling from a single view (i.e., the frontal view), called face alignment, and (ii) recognition module in the semantic face matching algorithm. Section 5.1 describes the concept of semantic facial components, the semantic face graph, the generic 3D face model, and interacting snakes (multiple snakes that interact with each other). Section 5.2 describes the coarse alignment between the semantic graph and the input image based on the results of face detection. Section 5.3 presents the process of fine alignment of the semantic graph using interacting snakes. We explain how to compute the matching scores for graph alignment, and then show the resultant facial sketches and cartoon faces. Section 5.4 describes a semantic face matching method for recognizing faces, the use of component weights based on alignment scores, and the cost function for face identification. Then we give the algorithm of the proposed semantic face matching. We illustrate the generated cartoon faces from aligned semantic face 111
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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
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2.3 Face Modeling Face modeling pla
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An advanced modeling approach which
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tionals to minimize, implementation
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are listed in Table 2.2. All of the
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low-level features can provide mean
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holistic 2D and geometrical 3D feat
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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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Page 121 and 122: 4.4 Model Construction Our face mod
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Page 171 and 172: Appendices
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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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