Emotion recognition has become one of the most active research areas in pattern recognition due to the emergence of human–machine interaction systems. Describing facial expression is a very challenging problem since it relies on the quality of the face representation. A multitude of features have been proposed in the literature to describe facial expression. None of these features is universal for accurately capturing all the emotions since facial expressions vary according to the person, gender and type of emotion (posed or spontaneous). Therefore, some research works have considered combining several features to enhance the recognition rate. But they faced significant problems because of information redundancy and high dimensionality of the resulting features. In this work, we propose a genetic programming framework for feature selection and fusion for facial expression recognition, which we called $GP-FER$. The main component of this framework is a tree-based genetic program with a three functional layers (feature selection, feature fusion and classification). The proposed genetic program is a binary classifier that performs discriminative feature selection and fusion differently for each pair of expression classes. The final emotion is captured by performing a unique tournament elimination between all the classes using the binary programs. Three different geometric and texture features were fused using the proposed $GP-FER$. The obtained results, on four posed and spontaneous facial expression datasets (DISFA, DISFA+, CK+ and MUG), show that the proposed facial expression recognition method has outperformed, or achieved a comparable performance to the state-of-the-art methods.

由于人机交互系统的出现，情绪识别已成为模式识别中最活跃的研究领域之一。描述面部表情是一个非常具有挑战性的问题，因为它依赖于面部表示的质量。在文献中已经提出了许多特征来描述面部表情。由于面部表情会根据人的性别，性别和类型（摆姿势或自发的）而有所不同，因此这些功能都无法准​​确捕捉所有情绪。因此，一些研究工作已经考虑将几种特征结合起来以提高识别率。但是由于信息冗余和所生成特征的高维度，它们面临着重大问题。在这项工作中，$GP-FË\mathrm{[R}$。该框架的主要组成部分是基于树的遗传程序，具有三个功能层（特征选择，特征融合和分类）。拟议的遗传程序是一个二元分类器，它对每对表达类别执行区别性特征选择和融合。通过使用二进制程序在所有班级之间进行独特的锦标赛淘汰来捕捉最终的情感。使用建议的融合了三种不同的几何和纹理特征$GP-FË\mathrm{[R}$。在四个姿势和自发的面部表情数据集（DISFA，DISFA +，CK +和MUG）上获得的结果表明，所提出的面部表情识别方法的性能优于或达到了与最新方法相当的性能。