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

By Kamlesh Mistry, Li Zhang, S. Neoh, Chee Peng Lim, Ben Fielding. IEEE Transactions on Cybernetics, 2017

316 citations recorded by the source corpus.

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 subdimension-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 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.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Urban Studies

View Paper: https://www.semanticscholar.org/paper/162da39da485c93fb3f65ce84b4136237376f65f

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In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022, methodology): “In [129], the authors used particle swarm optimization (PSO) in facial expression-based emotion recognition for feature selection.”