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

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caricatures by exaggerating the distinctiveness of facial components. After alignment, facial components are transformed to a feature space spanned by Fourier descriptors of facial components for face recognition, called semantic face matching. The matching algorithm computes the similarity between semantic face graphs of face templates in a database and a semantic face graph that is adapted to a given face image. The semantic face graph allows face matching based on selected facial components, and effective 3D model updating based on 2D face images. The results of our face matching demonstrate that the proposed face model can lead to classification and visualization (e.g., the generation of cartoon faces and facial caricatures) of human faces using the derived semantic face graphs. 1.6 Face Retrieval Today, people can accumulate a large number of images and video clips (digital content) because of the growing popularity of digital imaging devices, and because of the decreasing cost of high-capacity digital storage. This significant increase in the amount of digital content requires database management tools that allow people to easily archive and retrieve contents from their digital collections. Since humans and their activities are typically the subject of interest in consumers’ images and videos, detecting people and identifying them will help to automate image and video archival based on a high-level semantic concept, i.e., human faces. For example, we can design a system that manages digital content of personal photos and amateur videos based on the concept of human faces, e.g., “retrieve all images containing Carrie’s face.” 26
Using merely low-level features (e.g., skin color or color histograms) for retrieval and browsing is neither robust nor acceptable to the user. High level semantics have to be used to make such an image/video management system useful. Fig. 1.17 shows a graphical user interface of a facial feature-based retrieval system [18]. Figure 1.17. A face retrieval interface of the FACEit system [18]: the system gives the most similar face in a database given a query face image. In summary, the ability to group low-level features as a meaningful semantic entity is a critical issue in the retrieval of visual content. Accurately and efficiently detecting human faces plays a crucial role in facilitating face identification for managing face databases. In face recognition algorithms, the high-level concept–a human face–is implicitly expressed by face representations such as locations of feature points, surface texture, 2D graphs with feature nodes, 3D head surface, and combinations of them. The face representation plays an important role in the recognition process because 27
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
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Page 35 and 36: 17 Figure 1.12. System diagram of o
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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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