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

Sensors

Qi Jia²  Xinkai Gao²  He Guo¹  Zhongxuan Luo²  Yi Wang² 

[1] School of Software, Dalian University of Technology, Dalian 116621, China;

关键词: facial expression recognition;    local binary patterns;    weighted patches;    sparse representation;    multi-layer model;   

DOI  :  10.3390/s150306719

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.