By Stan Z. Li, Anil K. Jain
Even supposing the background of computer-aided face acceptance stretches again to the Sixties, computerized face reputation continues to be an unsolved challenge and nonetheless deals a very good problem to computer-vision and trend acceptance researchers. This guide is a complete account of face reputation examine and expertise, written through a bunch of prime overseas researchers. Twelve chapters conceal the entire sub-areas and significant parts for designing operational face popularity structures. historical past, smooth strategies, contemporary effects, and demanding situations and destiny instructions are thought of. The publication is aimed toward practitioners and pros making plans to paintings in face acceptance or eager to familiarize yourself with the state-of- the-art know-how. A finished instruction manual, by way of major learn specialists, at the ideas, tools, and algorithms for automatic face detection and popularity. crucial reference source for researchers and execs in biometric safety, laptop imaginative and prescient, and video photograph research.
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Additional info for Handbook Of Face Recognition
Li, L. Zhu, Z. Q. Zhang, A. Blake, H. Zhang, and H. Shum. Statistical learning of multi-view face detection. In Proceedings of the European Conference on Computer Vision, volume 4, pages 67–81, Copenhagen, Denmark, May 28 - June 2 2002. 21. Y. M. Li, S. G. Gong, and H. Liddell. Support vector regression and classiﬁcation based multi-view face detection and recognition. In IEEE Int. Conf. Oo Face & Gesture Recognition, pages 300–305, Grenoble, 2000. 22. R. Lienhart, A. Kuranov, and V. Pisarevsky.
Kanade. Object detection using the statistics of parts. International Journal of Computer Vision, 56(3):151–177, Feb 2004. 37. P. Y. Simard, L. Bottou, P. Haffner, and Y. L. Cun. Boxlets: a fast convolution algorithm for signal processing and neural networks. In M. Kearns, S. Solla, and D. Cohn, editors, Advances in Neural Information Processing Systems, volume 11, pages 571–577. MIT Press, 1998. 38. P. Y. Simard, Y. A. L. Cun, J. S. Denker, and B. Victorri. Transformation invariance in pattern recognition - tangent distance and tangent propagation.
Roth, and N. Ahuja. A SNoW-based face detector. In Proceedings of Neural Information Processing Systems, pages 855–861, 2000. 54. B. D. Zarit, B. J. Super, and F. K. H. Quek. Comparison of ﬁve color models in skin pixel classiﬁcation. In IEEE ICCV Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-time Systems, pages 58–63, Corfu, Greece, September 1999. Chapter 3. Modeling Facial Shape and Appearance Tim Cootes, Chris Taylor, Haizhuang Kang, Vladimir Petrovi´c Imaging Science and Biomedical Engineering, University of Manchester, UK To interpret images of faces, it is important to have a model of how the face can appear.