Feature space-based human face image representation and recognition

Yong Xu; Zizhu Fan; Qi Zhu

doi:10.1117/1.OE.51.1.017205

7 February 2012 Feature space-based human face image representation and recognition

Yong Xu, Zizhu Fan, Qi Zhu

Author Affiliations +

Optical Engineering, Vol. 51, Issue 1, 017205 (February 2012). https://doi.org/10.1117/1.OE.51.1.017205

Abstract

We propose a novel face recognition method that represents and classifies face images in the feature space. It first assumes that in the feature space the test sample can be well expressed by a linear combination of the training samples, and then it exploits the obtained linear combination to perform face recognition. We also present the foundation, rationale, and characteristics of, as well as the differences between, our method and conventional kernel methods. The analysis shows that our method is a representation-based kernel method and works in the feature space. This method might be able to outperform the representation-based methods that work in the original space. The experimental results show that our method partially possesses the properties of "sparseness" and is able to reduce greatly the effects of noise and occlusion in the test sample.

Citation Download Citation

Yong Xu, Zizhu Fan, and Qi Zhu "Feature space-based human face image representation and recognition," Optical Engineering 51(1), 017205 (7 February 2012). https://doi.org/10.1117/1.OE.51.1.017205

Published: 7 February 2012

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