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Journal of Zhejiang University SCIENCE A 2003 Vol.4 No.2 P.162-165


A color based face detection system using multiple templates

Author(s):  WANG Tao, BU Jia-Jun, CHEN Chun

Affiliation(s):  College of Computer Science and Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   wt@cs.zju.edu.cn

Key Words:  Color-based, Multiple templates matching, Face detection

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WANG Tao, BU Jia-Jun, CHEN Chun. A color based face detection system using multiple templates[J]. Journal of Zhejiang University Science A, 2003, 4(2): 162-165.

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A1 - CHEN Chun
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A color based system using multiple templates was developed and implemented for detecting human faces in color images. The algorithm consists of three image processing steps. The first step is human skin color statistics. Then it separates skin regions from non-skin regions. After that, it locates the frontal human face(s) within the skin regions. In the first step, 250 skin samples from persons of different ethnicities are used to determine the color distribution of human skin in chromatic color space in order to get a chroma chart showing likelihoods of skin colors. This chroma chart is used to generate, from the original color image, a gray scale image whose gray value at a pixel shows its likelihood of representing the skin. The algorithm uses an adaptive thresholding process to achieve the optimal threshold value for dividing the gray scale image into separate skin regions from non skin regions. Finally, multiple face templates matching is used to determine if a given skin region represents a frontal human face or not. Test of the system with more than 400 color images showed that the resulting detection rate was 83%, which is better than most color-based face detection systems. The average speed for face detection is 0.8 second/image (400×300 pixels) on a Pentium 3 (800MHz) PC.

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[1]Craw, I., Tock, D. and Bennett, A., 1992. Finding face features. In: Proceedings of the Second European Conference on Computer Vision, Italy, p.92-96.

[2]Kjeldsem, R. and Kender, J., 1996. Finding skin in color images. In: Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, IEEE Computer Society Press, Vermont, USA, p.312-317.

[3]Lanitis, A., Taylor, C. J. and Cootes, T. F., 1995. An automatic face identification system using flexible appearance models. Image and Vision Computing, 13(5):393-401.

[4]Ming, Y., David, K. and Narendra, A., 2001. Detecting faces in Images: A Survey.IEEE Transactions on Pattern Analysis and Machine Intelligance, 24(1):34-58.

[5]Osuna, E., Freund, R. and Girosi, F., 1997. Training support vector machines: An application to face detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico, p.130-136.

[6]Rowley, H., Baluja, S. and Kanade, T., 1998. Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1):23-38.

[7]Turk, M. and Pentland, A., 1991. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71-86.

[8]Yang, G. and Huang, T. S., 1994. Human face detection in complex background. Pattern Recognition, 27(1):53-63.

[9]Yang, J. and Waibel, A., 1996. A real-time face tracker. In: Proceedings of Third Workshop on Applications of Computer Vision, Sarasoto, USA, p.142-147.

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