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Open AccessArticle

Robust Facial Expression Recognition via Compressive Sensing

1
School of Physics and Electronic Engineering, Taizhou University, Taizhou 318000, China
2
Department of Computer Science, Taizhou University, Taizhou 318000, China
*
Author to whom correspondence should be addressed.
Sensors 2012, 12(3), 3747-3761; https://doi.org/10.3390/s120303747
Received: 28 December 2011 / Revised: 19 February 2012 / Accepted: 16 March 2012 / Published: 21 March 2012
(This article belongs to the Section Physical Sensors)
Recently, compressive sensing (CS) has attracted increasing attention in the areas of signal processing, computer vision and pattern recognition. In this paper, a new method based on the CS theory is presented for robust facial expression recognition. The CS theory is used to construct a sparse representation classifier (SRC). The effectiveness and robustness of the SRC method is investigated on clean and occluded facial expression images. Three typical facial features, i.e., the raw pixels, Gabor wavelets representation and local binary patterns (LBP), are extracted to evaluate the performance of the SRC method. Compared with the nearest neighbor (NN), linear support vector machines (SVM) and the nearest subspace (NS), experimental results on the popular Cohn-Kanade facial expression database demonstrate that the SRC method obtains better performance and stronger robustness to corruption and occlusion on robust facial expression recognition tasks. View Full-Text
Keywords: compressive sensing; sparse representation; facial expression recognition; Gabor wavelets representation; local binary patterns; corruption and occlusion compressive sensing; sparse representation; facial expression recognition; Gabor wavelets representation; local binary patterns; corruption and occlusion
MDPI and ACS Style

Zhang, S.; Zhao, X.; Lei, B. Robust Facial Expression Recognition via Compressive Sensing. Sensors 2012, 12, 3747-3761.

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