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

作者：Mistry, Kamlesh^*; Zhang, Li; Neoh, Siew Chin; Lim, Chee Peng; Fielding, Ben

来源：IEEE Transactions on Cybernetics, 2017, 47(6): 1496-1509.

DOI：10.1109/TCYB.2016.2549639

• Ensemble classifier
• facial expression recognition
• feature selection
• particle swarm optimization (PSO)

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.

• 出版日期2017-6