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

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tw(i, j, k) = emw(i, j, k) · ow(i, j, k) · q(i, j, k); (3.15) emw(i, j, k) = 1 (ew(i, j) + mw(k)); 2 (3.16) ew(i, j) = EyeMap(x i, y i ) + EyeMap(x j , y j ) ; 2 · EyeMap(x m , y m ) i > j; i, j ∈ [1, N eye ]; (3.17) mw(k) = MouthMap(x k, y k ) MouthMap(x m , y m ) ; k ∈ [1, N mth]; (3.18) ow(i, j, k) = 2∏ e −3(1−cos2 (θ r (i,j,k))) ; r=1 cos(θ r (i, j, k)) = ⃗u r · ⃗v r ‖⃗v r ‖ ; ‖⃗v r ‖ = 1. (3.19) Eq. (3.17) describes the eye-pair weight which is the normalized average of the eye map value around the two eyes, where EyeMap(x i , y i ) is the eye map value for the i-th eye candidate (associated with an eye blob and a corresponding pixel in the lowest level of the image pyramid). EyeMap(x m , y m ) is the eye map value for the most significant eye candidate (having the highest response within the eye map). The mouth weight, mw(k) in Eq. (3.18), is obtained by normalizing the mouth map value at the k-th mouth candidate (i.e., a mouth blob), MouthMap(x k , y k ), by the mouth map value at the most significant mouth candidate, MouthMap(x m , y m ). The faceorientation weight, described in Eq. (3.19), is the product of two attenuation terms, each of which is an exponential function of a projection (cosθ r ) of a vector (⃗v r ) along a particular direction ( ⃗u r ), where r = 1, 2. As can be seen in Fig. 3.13, one term favors a symmetric face, and it is a projection (cosθ 1 ) of the vector ⃗v 1 (from the midpoint of the two eyes to the mouth) along a vector ( ⃗u 1 ) that is perpendicular to the interocular 80
segment. The other term favors an upright face, and is a projection of a vector ⃗v 2 (from the mouth to the midpoint of the two eyes) along the vertical axis ( ⃗u 2 ) of the image plane. The exponential function, shown in Fig. 3.14, is designed such that the attenuation has the maximal value of 1 when θ 1 = θ 2 = 0 ◦ (i.e., when eyes and mouth form a letter “T” or equivalently the face is upright), and it decreases to below 0.5 at θ 1 = θ 2 = 25 ◦ . The quality of face boundary, q(i, j, k), can be directly obtained from the votes received by the best elliptical face boundary in the Hough transform. Eye i V 2 u 1 Eye j u 2 1 V 1 2 1 Mouth k Figure 3.13. Geometry of an eye-mouth triangle, where ⃗v 1 = −⃗v 2 ; unit vectors ⃗u 1 and ⃗u 2 are perpendicular to the interocular segment and the horizontal axis, respectively. Figure 3.14. Attenuation term, e −3(1−cos2 (θ r(i,j,k))) , plotted as a function of the angle θ r (in degrees) has a maximal value of 1 at θ r = 0 ◦ , and a value of 0.5 at θ r = 25 ◦ . 81
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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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Page 51 and 52: Chapter 2 Literature Review We firs
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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
Page 73 and 74: low-level features can provide mean
Page 75 and 76: holistic 2D and geometrical 3D feat
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
Page 85 and 86: (a) Figure 3.5. 2D projections of t
Page 87 and 88: 3.3 Localization of Facial Features
Page 89 and 90: (a) (b) (c) Figure 3.9. Constructio
Page 91 and 92: The eye map from the chroma is enha
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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
Page 103 and 104: (a) (b) (c) (d) (e) Figure 3.18. 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 and 116: Chapter 4 Face Modeling We first in
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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 127 and 128: (a) (b) (d) (e) (f) Figure 4.11. Te
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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Page 137 and 138: (a) (b) (c) (d) Figure 5.5. Boundar
Page 139 and 140: (a) (b) (c) (d) Figure 5.7. Coarse
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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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