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A multi-view face recognition system

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Abstract

In many automatic face recognition systems, posture constraining is a key factor preventing them from application. In this paper, a series of strategies-will be described to achieve a system which enables face recognition under varying pose. These approaches include the multi-view face modeling, the threshold image based face feature detection, the affine transformation based face posture normalization and the template matching based face identification. Combining all of these strategies, a face recognition system with the pose invariance is designed successfully. Using a 75MHZ Pentium PC and with a database of 75 individuals, 15 images for each person, and 225 test images with various postures, a very good recognition rate of 96.89% is obtained.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, 100084, Beijing

    Zhang Yongyue, Peng Zhenyun, You Suya & Xu Guangyou

Authors
  1. Zhang Yongyue
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  2. Peng Zhenyun
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  3. You Suya
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  4. Xu Guangyou
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Additional information

This work is supported by the National ‘863’ Hi-Tech Program of China (863–306)

Zhang Yongyue received his B.S. degree in Computer Engineering from Xi’an Jiaotong University in 1994. He is now a graduate student in Department of Computer Science & Technology, Tsinghua University. His research areas include computer vision, image processing and pattern recognition.

Peng Zhenyun is a Ph.D. candidate in Department of Computer Science & Technology, Tsinghua University. His research areas include computer vision and image processing.

You Suya is a post-doctoral researcher in Department of Computer Science and Technology, Tsinghua University. His research areas include computer vision and image processing.

Xu Guangyou is a Professor of Department of Computer Science & Technology, Tsinghua University. His research areas include computer vision, pattern recognition and multimedia technique.

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Zhang, Y., Peng, Z., You, S. et al. A multi-view face recognition system. J. of Comput. Sci. & Technol. 12, 400–407 (1997). https://doi.org/10.1007/BF02943172

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  • DOI: https://doi.org/10.1007/BF02943172

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