Face Processing: Advanced Modeling and Methods

Face Processing: Advanced Modeling and Methods

Language: English

Pages: 768

ISBN: B005H8CYPO

Format: PDF / Kindle (mobi) / ePub


Major strides have been made in face processing in the last ten years due to the fast growing need for security in various locations around the globe. A human eye can discern the details of a specific face with relative ease. It is this level of detail that researchers are striving to create with ever evolving computer technologies that will become our perfect mechanical eyes. The difficulty that confronts researchers stems from turning a 3D object into a 2D image. That subject is covered in depth from several different perspectives in this volume.

This book begins with a comprehensive introductory chapter for those who are new to the field. A compendium of articles follows that is divided into three sections. The first covers basic aspects of face processing from human to computer. The second deals with face modeling from computational and physiological points of view. The third tackles the advanced methods, which include illumination, pose, expression, and more. Editors Zhao and Chellappa have compiled a concise and necessary text for industrial research scientists, students, and professionals working in the area of image and signal processing.

*Contributions from over 35 leading experts in face detection, recognition and image processing
*Over 150 informative images with 16 images in FULL COLOR illustrate and offer insight into the most up-to-date advanced face processing methods and techniques
*Extensive detail makes this a need-to-own book for all involved with image and signal processing

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points, edges, corners, lines, or contours are tracked over a sequence of frames, and the depths of these features are computed. To overcome the difficulty 26 Chapter 1: A GUIDED TOUR OF FACE PROCESSING of feature tracking, bundle adjustment [157] can be used to obtain better and more robust results. Recently, multiview-based 2D methods have gained popularity. In [125], a model consists of a sparse 3D shape model learned from 2D images labeled with pose and landmarks, a shape-and-pose-free

still-image-based face recognition: the abundance of temporal information. However, the typically low-quality images in video present a significant challenge: reduced spatial information. The key to building a successful Section 1.4: METHODS FOR FACE RECOGNITION Table 1.3: 27 Categorization of video-based face-recognition techniques Approach Examples Still-image methods Basic methods [73, 65, 49, 129, 62] Tracking-enhanced [87, 88, 83] Video- and audio-based [81, 82]

structure from texture. Media Lab Technical Report 440, MIT, 1998. [121] M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. In: Proc. of International Conference on Computer Vision, pages 259–268, 1987. [122] M. Kirby and L. Sirovich. Application of the Karhunen–Loeve procedure for the characterization of human faces. IEEE Trans. on Pattern Analysis and Machine Intelligence, 12, 1990. [123] M. Lades, J. Vorbruggen, J. Buhmann, J. Lange, C. v. Malsburg, R. Wurtz, and W. Konen.

hemispheric asymmetry in man and monkey: evidence from single unit and reaction time studies,” Behavioural Brain Research 29, pp. 245–258, 1988. [79] R. Desimone, T. D. Albright, C. G. Gross, and C. J. Bruce, “Stimulus-selective properties of inferior temporal neurons in the macaque,” Neuroscience 4, pp. 2051–2068, 1984. [80] E. T. Rolls, G. C. Baylis, M. E. Hasselmo, and V. Nalwa, “The effect of learning on the face selective responses of neurons in the cortex in the superior temporal sulcus of

invariant signatures for surfaces, IEEE Trans. PAMI 25 (2003), no. 10, 1285–1295. A. S. Georghiades, P. N. Belhumeur, and D.J. Kriegman, Illumination cones for recognition under variable lighting: faces, Proc. CVPR, 1998, pp. 52–58. A. Goldstein, L. Harmon, and A. Lesk, Identification of human faces, Proc. IEEE 59 (1971), no. 5, 748–760. G. H. Golub and C. F. van Loan, Matrix Computations, third ed., The John Hopkins University Press, 1996. G. Gordon, Face recognition based on depth and curvature

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