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

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Table 2.1 Summary of various face detection approaches. Authors Year Approach Features Used Féraud et al. [19] DeCarlo et al. [61] Maio et al. [20] Frontal Video images Abdel- Mottaleb et al. [84] Garcia et al. [21] Wu et [85] al. Rowley et al. [24], [23] Sung et al. [25] Colmenarez et al. [86] Yow et al. [26] Lew et al. [27] 2000 Optical flow 2000 Facial templates; Hough transform 1999 Skin model; Feature 1999 Statistical wavelet analysis 1999 Fuzzy color models; Template matching 1998 Neural networks Motion; Color; Texture Motion; Edge; Deformable face model; Texture Texture; Directional images Color Color; Wavelet coefficients Color Texture Head Pose 2001 Neural networks Frontal to profile Frontal to profile Test Databases Sussex; CMU; Web images Videos Frontal to profile Frontal to near frontal Frontal to profile (Upright) frontal Minimal Face Size 15 × 20 NA 20 × 27 HHI 13 × 13 MPEG videos Still color images FERET; CMU; Web images 1998 Learning Texture Frontal Video images; newspaper scans 1997 Learning Markov processes 1997 Feature; Geometrical Belief facial networks features 1996 Markov random field; DFFS [64] Most informative pixel 34 80 × 48 20 × 24 20 × 20 19 × 19 Frontal FERET 11 × 11 Frontal to profile Frontal CMU 60 × 60 MIT; CMU; Leiden 23 × 32
mation theory, geometrical modeling, (deformable) template matching, Hough transform, extraction of geometrical facial features, motion extraction, and color analysis. Typical detection outputs are shown in Fig. 2.1. In these images, a detected face is usually overlaid with graphical objects such as a rectangle or an ellipse for a face, and circles or crosses for eyes. The neural network-based [24], [23] and the viewbased [25] approaches require a large number of face and non-face training examples, and are designed primarily to locate frontal faces in grayscale images. It is difficult to enumerate “non-face” examples for inclusion in the training databases. Schneiderman and Kanade [22] extend their learning-based approach for the detection of frontal faces to profile views. A feature-based approach combining geometrical facial features with belief networks [26] provides face detection for non-frontal views. Geometrical facial templates and the Hough transform were incorporated to detect grayscale frontal faces in real time applications [20]. Face detectors based on Markov random fields [27], [87] and Markov chains [88] make use of the spatial arrangement of pixel gray values. Model based approaches are widely used in tracking faces and often assume that the initial location of a face is known. For example, assuming that several facial features are located in the first frame of a video sequence, a 3D deformable face model was used to track human faces [61]. Motion and color are very useful cues for reducing search space in face detection algorithms. Motion information is usually combined with other information (e.g., face models and skin color) for face detection and tracking [89]. A method of combining a Hidden Markov Model (HMM) and motion for tracking was presented in [86]. A combination of motion and color filters, and a neural network model was proposed in [19]. 35
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-
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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
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Page 51: Chapter 2 Literature Review We firs
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Page 87 and 88: 3.3 Localization of Facial Features
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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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