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

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ecognizing a single (or multiple) human subject(s) or face(s). Face recognition is an important biometric identification technology. Facial scan is an effective biometric attribute/indicator. Different biometric indicators are suited for different kinds of identification applications due to their variations in intrusiveness, accuracy, cost, and (sensing) effort [5] (see Fig. 1.2(a)). Among the six biometric indicators considered in [6], facial features scored the highest compatibility, shown in Fig. 1.2(b), in a machine readable travel documents (MRTD) system based on a number of evaluation factors, such as enrollment, renewal, machine requirements, and public perception [6]. 1.1 Challenges in Face Recognition Humans can easily recognize a known face in various conditions and representations (see Fig. 1.3). Such a remarkable ability of humans to recognize faces with large intra-subject variations has inspired vision researchers to develop automated systems for face recognition based on 2D face images. However, the current state-of-the-art machine vision systems can recognize faces only in a constrained environment. Note that there are two types of face comparison scenarios, called (i) face verification (or authentication) and (ii) face identification (or recognition). As shown in Fig. 1.4, face verification involves a one-to-one match that compares a query face image against a template face image whose identity is being claimed, while face identification involves one-to-many matches that compare a query face image against all the template images in a face database to determine the identity of the query face. The main chal- 2
(a) (b) Figure 1.1. Applications using face recognition technology: (a) and (b) automated video surveillance (downloaded from Visionics [1] and FaceSnap [2], respectively); (c) and (d) access control (from Visionics [1] and from Viisage [3], respectively); (e) management of photo databases (from Viisage [3]); (f) multimedia communication (from Eyematic [4]). Images in this dissertation are presented in color. 3
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: Chapter 1 Introduction In recent ye
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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 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
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Page 65 and 66: 2.3 Face Modeling Face modeling pla
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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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size) to generate potential face ca
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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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