Neural Computing and Applications

, Volume 28, Issue 2, pp 233–244 | Cite as

Face recognition based on random subspace method and tensor subspace analysis

Original Article
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Abstract

In this paper, we propose a novel method, called random subspace method (RSM) based on tensor (Tensor-RS), for face recognition. Different from the traditional RSM which treats each pixel (or feature) of the face image as a sampling unit, thus ignores the spatial information within the face image, the proposed Tensor-RS regards each small image region as a sampling unit and obtains spatial information within small image regions by using reshaping image and executing tensor-based feature extraction method. More specifically, an original whole face image is first partitioned into some sub-images to improve the robustness to facial variations, and then each sub-image is reshaped into a new matrix whose each row corresponds to a vectorized small sub-image region. After that, based on these rearranged newly formed matrices, an incomplete random sampling by row vectors rather than by features (or feature projections) is applied. Finally, tensor subspace method, which can effectively extract the spatial information within the same row (or column) vector, is used to extract useful features. Extensive experiments on four standard face databases (AR, Yale, Extended Yale B and CMU PIE) demonstrate that the proposed Tensor-RS method significantly outperforms state-of-the-art methods.

Keywords

Face recognition Random subspace method (RSM) Tensor subspace analysis Ensemble learning Sub-image method 
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Notes

Acknowledgments

This work was fully supported by the NUAA Research Funding (No. NS2014100).

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Copyright information

© The Natural Computing Applications Forum 2015

Authors and Affiliations

  1. 1.Center of ComputerNanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China

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