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More robust face recognition by considering occlusion information

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4 Author(s)
Rama, A. ; Dept. of Signal Theor. & Commun., Tech. Univ. of Catalonia ; Tarres, F. ; Goldmann, L. ; Sikora, T.

This paper addresses one of the main challenges of face recognition (FR): facial occlusions. Currently, the human brain is the most robust known FR approach towards partially occluded faces. Nevertheless, it is still not clear if humans recognize faces using a holistic or a component-based strategy, or even a combination of both. In this paper, three different approaches based on principal component analysis (PCA) are analyzed. The first one, a holistic approach, is the well-known eigenface approach. The second one, a component-based method, is a variation of the eigenfeatures approach, and finally, the third one, a near-holistic method, is an extension of the lophoscopic principal component analysis (LPCA). So the main contributions of this paper are: The three different strategies are compared and analyzed for identifying partially occluded faces and furthermore it explores how a priori knowledge about present occlusions can be used to improve the recognition performance.

Published in:

Automatic Face & Gesture Recognition, 2008. FG '08. 8th IEEE International Conference on

Date of Conference:

17-19 Sept. 2008