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

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List of Tables 2.1 Summary of various face detection approaches. . . . . . . . . . . . . . . . 34 2.2 Geometric compression efficiency. . . . . . . . . . . . . . . . . . . . . . . 53 2.3 Summary of performance of various face detection approaches. . . . . . . 56 3.1 Detection results on the HHI image database (Image size 640 × 480) on a PC with 1.7 GHz CPU. FP: False Positives, DR: Detection Rate. . . 88 3.2 Detection results on the Champion database (Image size ∼ 150 × 220) on a PC with 860 MHz CPU. FP: False Positives, DR: Detection Rate. . 88 5.1 Error rates on a 50-image database. . . . . . . . . . . . . . . . . . . . . . 136 5.2 Dimensions of the semantic graph descriptors for individual facial components. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 x
List of Figures 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 1.1 (Cont’d). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1 (Cont’d). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 Comparison of various biometric features: (a) based on zephyr analysis (downloaded from [5]); (b) based on MRTD compatibility (from [6]). . 5 1.3 Intra-subject variations in pose, illumination, expression, occlusion, accessories (e.g., glasses), color, and brightness. . . . . . . . . . . . . . . . 6 1.4 Face comparison: (a) face verification/authentication; (b) face identification/recognition. Face images are taken from the MSU face database [7]. 6 1.5 Head recognition versus face recognition: (a) Clinton and Gore heads with the same internal facial features, adapted from [8]; (b) two faces of different subjects with the same internal facial components show the important role of hair and face outlines in human face recognition. . . 8 1.6 Caricatures of (a) Vincent Van Gogh; (b) Jim Carrey; (c) Arnold Schwarzenegger; (d) Einstein; (e) G. W. Bush; and (f) Bill Gates. Images are downloaded from [9], [10] and [10]. Caricatures reveal the use of component weights in face identification. . . . . . . . . . . . . 9 1.7 Cartoons reveal that humans can easily recognize characters whose facial components are depicted by simple line strokes and color characteristics: (a) and (b) are frames adapted from the movie Pocahontas; (c) and (d) are frames extracted from the movie Little Mermaid II. (Disney Enterprises, Inc.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 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). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 xi
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: 4 Face Modeling 97 4.1 Modeling Met
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 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
Page 57 and 58: esult in different recognition appr
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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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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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