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Proceedings Paper

Human face recognition method based on the statistical model of small sample size
Author(s): Yong-Qing Cheng; Ke Liu; Jingyu Yang; Yong-Ming Zhuang; Nian-Chun Gu
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Paper Abstract

Automatic recognition of human faces is a frontier topic in computer vision. In this paper, a novel recognition approach to human faces is proposed, which is based on the statistical model in the optimal discriminant space. Singular value vector has been proposed to represent algebraic features of images. This kind of feature vector has some important properties of algebraic and geometric invariance, and insensitiveness to noise. Because singular value vector is usually of high dimensionality, and recognition model based on these feature vectors belongs to the problem of small sample size, which has not been solved completely, dimensionality compression of singular value vector is very necessary. In our method, an optimal discriminant transformation is constructed to transform an original space of singular value vector into a new space in which its dimensionality is significantly lower than that in the original space. Finally, a recognition model is established in the new space. Experimental results show that our method has very good recognition performance, and recognition accuracies of 100 percent are obtained for all 64 facial images of 8 classes of human faces.

Paper Details

Date Published: 1 February 1992
PDF: 11 pages
Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); doi: 10.1117/12.57049
Show Author Affiliations
Yong-Qing Cheng, East China Institute of Technology (China)
Ke Liu, East China Institute of Technology (China)
Jingyu Yang, East China Institute of Technology (China)
Yong-Ming Zhuang, East China Institute of Technology (China)
Nian-Chun Gu, East China Institute of Technology (China)


Published in SPIE Proceedings Vol. 1607:
Intelligent Robots and Computer Vision X: Algorithms and Techniques
David P. Casasent, Editor(s)

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