IEEE Trans Cybern. 2017 Jun;47(6):1496-1509. doi: 10.1109/TCYB.2016.2549639. Epub 2016 Apr 21.
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.
本文提出了一种基于进化粒子群优化（PSO）的特征优化的面部表情识别系统。该系统首先采用改进的局部二值模式，进行水平和垂直邻域像素比较，生成有区别的初始面部表示。然后，提出了一种嵌入微遗传算法（mGA）概念的 PSO 变体，称为 mGA-embedded PSO，用于执行特征优化。它包含一个不可替代的记忆、一个小种群的二级群、一个新的速度更新策略、一个基于子维度的深入局部面部特征搜索，以及一个局部开发和全局探索搜索机制的合作，以减轻传统 PSO 的过早收敛问题。多个分类器用于识别七种面部表情。基于在扩展的 Cohn Kanade 和 MMI 基准数据库中的内部和跨域图像的综合研究，实验结果表明，我们提出的系统在识别率方面显著优于其他最先进的 PSO 变体、传统 PSO、经典 GA 以及文献中报道的其他相关面部表情识别模型。
