A Hybrid of Principal Component Analysis and Partial Least Squares for Face Recognition across Pose
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.
KeywordsFace recognition across pose Partial least squares Principal component analysis Hybrid-eigenfaces Linear regression
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