title: A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition.
abstract: 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.
authors.forename: Kamlesh
authors.initials: K
authors.lastname: Mistry
authors.forename: Li
authors.initials: L
authors.lastname: Zhang
authors.forename: Siew Chin
authors.initials: SC
authors.lastname: Neoh
authors.forename: Chee Peng
authors.initials: CP
authors.lastname: Lim
authors.forename: Ben
authors.initials: B
authors.lastname: Fielding
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D004644:Emotions / Q000145:classification*; D005145:Face / Q000033:anatomy & histology / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008957:Models, Genetic; D010363:Pattern Recognition, Automated / Q000379:methods*