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Sensors 2015, 15(3), 6719-6739; doi:10.3390/s150306719

Multi-Layer Sparse Representation for Weighted LBP-Patches Based Facial Expression Recognition

School of Software, Dalian University of Technology, Dalian 116621, China
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Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 15 December 2014 / Revised: 28 February 2015 / Accepted: 10 March 2015 / Published: 19 March 2015
(This article belongs to the Section Physical Sensors)
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Abstract

In this paper, a novel facial expression recognition method based on sparse representation is proposed. Most contemporary facial expression recognition systems suffer from limited ability to handle image nuisances such as low resolution and noise. Especially for low intensity expression, most of the existing training methods have quite low recognition rates. Motivated by sparse representation, the problem can be solved by finding sparse coefficients of the test image by the whole training set. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. We evaluate facial representation based on weighted local binary patterns, and Fisher separation criterion is used to calculate the weighs of patches. A multi-layer sparse representation framework is proposed for multi-intensity facial expression recognition, especially for low-intensity expressions and noisy expressions in reality, which is a critical problem but seldom addressed in the existing works. To this end, several experiments based on low-resolution and multi-intensity expressions are carried out. Promising results on publicly available databases demonstrate the potential of the proposed approach. View Full-Text
Keywords: facial expression recognition; local binary patterns; weighted patches; sparse representation; multi-layer model facial expression recognition; local binary patterns; weighted patches; sparse representation; multi-layer model
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Jia, Q.; Gao, X.; Guo, H.; Luo, Z.; Wang, Y. Multi-Layer Sparse Representation for Weighted LBP-Patches Based Facial Expression Recognition. Sensors 2015, 15, 6719-6739.

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