A micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition

Mistry, Kamlesh, Zhang, Li, Neoh, Siew Chin, Lim, Chee Peng and Fielding, Ben (2017) A micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition. IEEE Transactions on Cybernetics, 47 (6). pp. 1496-1509. ISSN 2168-2267

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Official URL: http://dx.doi.org/10.1109/TCYB.2016.2549639

Abstract

This research proposes a facial expression recognition system using evolutionary Particle Swarm Optimization (PSO)-based feature optimization. The system first employs modified Local Binary Patterns, which conduct horizontal and vertical neighbourhood pixel comparison, to generate a discriminative initial facial representation. Then, a PSO variant embedded with the concept of a micro Genetic Algorithm (mGA), called mGA-embedded PSO, is proposed to perform feature optimization. It incorporates a non-replaceable memory, a small-population secondary swarm, a new velocity updating strategy, a sub-dimension based in-depth local facial feature search, and a cooperation of local exploitation and global exploration search mechanism to mitigate the premature convergence problem of conventional PSO. Multiple classifiers are used for recognizing seven facial expressions. Based on a comprehensive study using within- and cross-domain images from the CK+ and MMI benchmark databases, respectively, the empirical results indicate that our proposed system outperforms other state-of-the-art PSO variants, conventional PSO, classical GA, and other related facial expression recognition models reported in the literature by a significant margin.

Item Type: Article
Uncontrolled Keywords: Ensemble classifier, facial expression recognition, feature selection, particle swarm optimization.
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: Becky Skoyles
Date Deposited: 01 Apr 2016 08:22
Last Modified: 17 Aug 2017 11:28
URI: http://nrl.northumbria.ac.uk/id/eprint/26487

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