人脸表情识别文章 A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognitio
Abstract—This paper proposes a facial expression recognition system using evolutionary particle swarm optimization (PSO)-based feat 4000 ure 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 mGAembedded 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.
总结:
1.这是用嵌入了遗传算法的PSO(mGA-PSO),对7种人脸表情进行分类。
2. LBP做特征提取,mGA-PSO做特征优化(特征选取),分类器做分类。
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