Intelligent Facial Emotion Recognition

Abstract

The system introduces an intelligent facial emotion recognition using arti?cial neural network (ANN). The concept ?rst takes modi?ed local binary patterns, which involve horizontal vertical and neighborhood pixel comparison, to produce initial facial representation. Then, a microgenetic algorithm(mGA) embedded Particle Swarm Optimization(PSO), is proposed for feature optimization. It incorporates a nonreplaceable 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. Arti?cial Neural Network is used as a classi?er for recognizing seven facial emotions. ANN is implemented as classi?er for pattern recognition. Based on a comprehensive study using within- and cross-domain images from the extended Japanese database. 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.