Facial expression recognition via weighted group sparsity

doi:10.1007/s11704-016-5204-4

Front. Comput. Sci.

2017, Vol. 11

Issue (2) : 266-275 https://doi.org/10.1007/s11704-016-5204-4

RESEARCH ARTICLE

Facial expression recognition via weighted group sparsity

Hao ZHENG^1,²(

),Xin GENG²

¹. Key Laboratory of Trusted Cloud Computing and Big Data Analysis, School of Information Engineering, Nanjing Xiaozhuang University, Nanjing 211171, China
². School of Computer Science and Engineering, Southeast University, Nanjing 211189, China

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Abstract

Considering the distinctiveness of different group features in the sparse representation, a novel joint multitask and weighted group sparsity (JMT-WGS) method is proposed. By weighting popular group sparsity, not only the representation coefficients from the same class over their associate dictionaries may share some similarity, but also the representation coefficients from different classes have enough diversity. The proposed method is cast into a multi-task framework with two-stage iteration. In the first stage, representation coefficient can be optimized by accelerated proximal gradient method when the weights are fixed. In the second stage, the weights are computed via the prior information about their entropy. The experimental results on three facial expression databases show that the proposed algorithm outperforms other state-of-the-art algorithms and demonstrate the promising performance of the proposed algorithm.

Keywords facial expression recognition multi-task learning group sparsity

Corresponding Author(s): Hao ZHENG

Just Accepted Date: 30 September 2016 Online First Date: 23 March 2017 Issue Date: 06 April 2017

Cite this article:

Hao ZHENG,Xin GENG. Facial expression recognition via weighted group sparsity[J]. Front. Comput. Sci., 2017, 11(2): 266-275.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-016-5204-4
https://academic.hep.com.cn/fcs/EN/Y2017/V11/I2/266

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