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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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 |
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Uncontrolled Keywords: | Ensemble classifier, facial expression recognition, feature selection, particle swarm optimization. |
Subjects: | G400 Computer Science |
Department: | Faculties > Engineering and Environment > Computer and Information Sciences |
Depositing User: | Becky Skoyles |
Date Deposited: | 01 Apr 2016 08:22 |
Last Modified: | 01 Aug 2021 08:38 |
URI: | http://nrl.northumbria.ac.uk/id/eprint/26487 |
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