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

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with a distinctive chin no matter whether the face appears in a caricature (e.g., Jim Carrey shown in Fig. 1.6(b)) or in a real photo [52]. Caricatures reveal that there are certain facial features which are salient for each individual and that a relatively easier identification of faces can occur by emphasizing distinctive facial components (using weights) and their configuration. Besides, the spatial configuration of facial components has been shown to take a more important role in face recognition than local texture by using inverted faces [53] in which the (upright) face recognition is disrupted (see Fig. 1.8). Therefore, we group these salient facial components [48] as (a) (b) (c) Figure 1.8. Configuration of facial components: (a) face image; (b) face image in (a) with enlarged eyebrow-to-eye and nose-to-mouth distances; (c) inverted face of the image in (b). A small change of component configuration results in a significantly different facial appearance in an upright face in (b); however, this change may not be perceived in an inverted face in (c). a graph and derive component weights in our face matching algorithm to improve the recognition performance. In addition, humans can recognize faces in the presence of occlusions, i.e., face recognition can be based on a (selected) subset of facial components. This explains 10
the motivation for studies that attempt to recognize faces from eyes only [54]. The use of component weights can facilitate face recognition based on selected facial components. Furthermore, the shape of facial components (see Fig. 1.9(a)) has been used in physiognomy (or face reading, an ancient art of deciphering a person’s past and personality from his/her face). In light of this art, we design a semantic face graph for face recognition (see in Chapter 5), shown in Fig. 1.9(b), in which ten facial components are filled with different shades in a frontal view. (a) Figure 1.9. Facial features/components: (a) five kinds of facial features (i.e., eyebrows, eyes, nose, ears, and mouth) in a face for reading faces in physiognomy (downloaded from [11]); (b) a frontal semantic face graph, whose nodes are facial components that are filled with different shades. (b) For each facial component, the issue of representation also plays an important role in face recognition. It has been believed that local facial texture and shading are crucial for recognition [52]. However, some frames of a cartoon video, as shown in Fig. 11
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: can recognize known faces in carica
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 61 and 62: (a) (b) (c) (d) Figure 2.4. Interna
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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
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oundary. A face score is computed f
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normalized red-green (r-g) space [1
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(a) (b) Figure 3.3. The Y C b C r c
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(a) Figure 3.5. 2D projections of t
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3.3 Localization of Facial Features
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(a) (b) (c) Figure 3.9. Constructio
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The eye map from the chroma is enha
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mouth is performed within the face
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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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