Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 47, Issue 6, Pages 1496-1509Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2016.2549639
Keywords
Ensemble classifier; facial expression recognition; feature selection; particle swarm optimization (PSO)
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Funding
- European Union through the (Erasmus Mundus) Centre of Excellence for Learning, Innovation, Networking and Knowledge Project [2645]
- Higher Education Innovation Fund
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This paper 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 neighborhood 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 nonreplaceable memory, a small-population secondary swarm, a new velocity updating strategy, a subdimensionbased 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 extended Cohn Kanade 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.
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