A novel face recognition method based on sub-pattern and tensor

Authors:
Su-Jing Wang;Chun-Guang Zhou;Yu-Hsin Chen;Xu-Jun Peng;Hui-Ling Chen;Gang Wang;Xiaohua Liu
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China;Raytheon BBN Technologies, Boston, MA 02138, USA;College of Computer Science and Technology, Jilin University, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China
Venue:
Neurocomputing
Year:
2011

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Abstract

This paper aims to address one of the many problems existing in current facial recognition techniques using tensor (TensorFace Algorithm and its extensions). Current methods rasterize facial images as vectors, which result in a loss of spatial structure information of facial images. In this paper, we propose a method called Sp-Tensor to extend TensorFace by applying the sub-pattern technique. Advantages of the proposed method include: (1) a portion of spatial structure and local information of facial images is preserved; (2) dramatically reduce the computation complexity than other existing methods when building the model. The experimental results demonstrate that Sp-Tensor has better performance than the original TensorFace and Sp-PCA1, especially for facial images with un-modeled views and light conditions.