In this paper, we propose a simple and efficient hybrid approach based on the combination of principal component analysis and partial least squares. Principal component analysis is used to reduce the dimension of image in first step and partial least squares method is used to carry out pose invariant face recognition in second step. The performance of proposed method is compared with another popular method based on global linear regression on hybrid-eigenface (HGLR) in terms of classification accuracy and computation time. Experimental results on two well known publicly available face databases demonstrate the effectiveness of the proposed approach. © 2012 Springer-Verlag.
CITATION STYLE
Jaiswal, A., Kumar, N., & Agrawal, R. K. (2012). A hybrid of principal component analysis and partial least squares for face recognition across pose. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7441 LNCS, pp. 67–73). https://doi.org/10.1007/978-3-642-33275-3_8
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