Abstract
In this paper, we propose a novel occlusion invariant face recognition algorithm based on Selective Local Nonnegative Matrix Factorization (S-LNMF) technique. The proposed algorithm is composed of two phases; the occlusion detection phase and the selective LNMF-based recognition phase. We use local approach to effectively detect partial occlusion in the input face image. A face image is first divided into a finite number of disjointed local patches, and then each patch is represented by PCA (Principal Component Analysis), obtained by corresponding occlusion-free patches of training images. And 1-NN threshold classifier was used for occlusion detection for each patch in the corresponding PCA space. In the recognition phase, by employing the LNMF-based face representation, we exclusively use the LNMF bases of occlusion-free image patches for face recognition. Euclidean nearest neighbor rule is applied for the matching. Experimental results demonstrate that the proposed local patch-based occlusion detection technique and S-LNMF-based recognition algorithm works well and the performance is superior to other conventional approaches.
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© 2006 Springer-Verlag Berlin Heidelberg
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Oh, H.J., Lee, K.M., Lee, S.U., Yim, CH. (2006). Occlusion Invariant Face Recognition Using Selective LNMF Basis Images. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_13
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DOI: https://doi.org/10.1007/11612032_13
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