Two-Stage Tensor Locality-Preserving Projection Face Recognition

Abstract

Locality-preserving projection (LPP) is an efficient dimensionality reduction approach that preserves local relationships within data sets and uncovers essential manifold structures. In this paper, we develop a two-stage tensor locality-preserving projection for face recognition, in which first-stage tensor LPP is performed in the original tensor space of face images and second stage tensor LPP is performed in the reduced-dimension tensor subspace of the first-stage projection. For classification, we seek a non-negative sparse representation in the final low-dimensional tensor subspace and determine the class of an unknown face image by minimum sparse representation error. Experimental studies demonstrate that our proposed two-stage tensor LPP scheme along with the non-negative sparse representation classifier effectively exploits the locality structure of face images and outperforms existing state-of-the-art face recognition schemes.

DOI: 10.1109/BigMM.2016.31

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Cite this paper

@article{Liu2016TwoStageTL, title={Two-Stage Tensor Locality-Preserving Projection Face Recognition}, author={Ying Liu and Dimitris A. Pados and Chia-Hung Yeh}, journal={2016 IEEE Second International Conference on Multimedia Big Data (BigMM)}, year={2016}, pages={214-218} }