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

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2.4 Face Retrieval Face recognition technology provides a useful tool for content-based image and video retrieval using the concept of human faces. Based on face detection and identification technology, we can design a system for consumer photo management (or for web graphic search) that uses human faces for indexing and retrieving image content and generates annotation (textual descriptions) for the image content automatically. Traditional text-based retrieval systems for digital libraries can not fulfill a retrieval of visual content such as human faces, eye shape, and cars in image or video databases. Hence, many researchers have been developing multimedia retrieval techniques based on automatically extracting salient features from the visual content (see [40] for an extensive review). Well known systems for content-based image and video retrieval are QBIC [142], Photobook [143], CONIVAS [144], FourEyes [145], Virage [146], ViBE [147], VideoQ [148], Visualseek [149], Netra [150], MARS [151], PicSOM [152], ImageScape [153], etc. In these systems, retrieval is performed by comparing a set of low-level features of a query image or video clip with features stored in the database and then by presenting the user with the content that has the most similar features. However, users normally query an image or video database based on semantics rather than low-level features. For example, a typical query might be specified as “retrieve images of fireworks” rather than “retrieve images that have large dark regions and colorful curves over the dark regions”. Since the commonly used features are usually a set of unorganized low-level attributes (such as color, texture, geometrical shape, layout, and motion), grouping 54
low-level features can provide meaningful high-level semantics for human consumers. There has been some work done on automatically classifying images into semantic categories [154], such as indoors/outdoors and city/landscape images. As for the semantic concept of faces, the generic facial topology (e.g., our proposed generic semantic face graph) is a useful structure for representing the face in a search engine. We have designed a graphical user interface for face editing using our face detection algorithm. Combined with our semantic face matching algorithm, we can build a face retrieval system. 2.5 Summary We have briefly described the development of face detection, face recognition, face modeling and model compression in this chapter. We have summarized the performance of currently available face detection systems in Table 2.3. Note that the performance of a detection system depends on several factors such as face databases on which the system is evaluated, system architecture, distance metric, and algorithmic parameters. The performance is evaluated based on the detection rate, the false positive rate (false acceptance rate), and databases. In Table 2.3, we do not include the false acceptance rate because the false positive rate has not been completely reported in literature. We refer the reader to the FERET evaluation [68], [28] for the performance of various face recognition systems. Face detection and face recognition are closely related to each other in the sense of categorizing faces. Over the past ten years, based on the statistical pattern theory, 55
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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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Page 51 and 52: Chapter 2 Literature Review We firs
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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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