Local Linear Regression on Hybrid Eigenfaces for Pose Invariant Face Recognition

Authors:
Ajay Jaiswal;Nitin Kumar;R. K. Agrawal
Affiliations:
Jawaharlal Nehru University, India;Jawaharlal Nehru University, India;Jawaharlal Nehru University, India
Venue:
International Journal of Computer Vision and Image Processing
Year:
2012

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Abstract

Pose variation leads to significant decline in the performance of the face recognition systems. In this paper, the authors propose a new approach HLLR, based on conjunction of hybrid-eigenfaces and local linear regression LLR, to perform face recognition across pose. In this approach, LLR on hybrid-eigenfaces is used to generate virtual views. These virtual views in frontal and non-frontal poses are obtained using frontal gallery image. The performance of the proposed approach is compared for classification accuracy with another efficient method based on global linear regression on hybrid eigenface HGLR. They also investigate the effect of number of images used to construct hybrid-eigenfaces on classification accuracy. Experimental results on two well known publicly available face databases demonstrate the effectiveness of the proposed approach. The suitability of proposed approach is also noticed when the number of available images is small.