Abstract
In this paper, we propose an adaptively weighted subpattern-based isometric projection (Aw-spIsoP) algorithm for face recognition. Unlike IsoP (isometric projection) based on a whole image pattern, the proposed Aw-spIsoP method operates on sub-patterns partitioned from an original whole face image and separately extracts corresponding local sub-features from them. Moreover, the adjacency graph used in the algorithm is constructed based on path-based distance optimized neighborhoods of the sub-patterns and the contribution of each sub-pattern is adaptively computed in order to enhance the robustness to facial pose, expression and illumination variations. Experimental results on three bench mark face databases (ORL, YALE and PIE) show that Aw-spIsoP can overcome the shortcomings of the existed subpattern-based methods and achieve the promising recognition accuracy.
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Wei, L., Zeng, W., Xu, F. (2011). Adaptively Weighted Subpattern-Based Isometric Projection for Face Recognition. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_68
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DOI: https://doi.org/10.1007/978-3-642-23896-3_68
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