In this paper, we report an effective facial expression recognition system for classifying six or seven basic expressions accurately. Instead of using the whole face region, we define three kinds of active regions, i.e., left eye regions, right eye regions and mouth regions. We propose a method to search optimized active regions from the three kinds of active regions. A Convolutional Neural Network (CNN) is trained for each kind of optimized active regions to extract features and classify expressions. In order to get representable features, histogram equalization, rotation correction and spatial normalization are carried out on the expression images. A decision-level fusion method is applied, by which the final result of expression recognition is obtained via majority voting of the three CNNs’ results. Experiments on both independent databases and fused database are carried out to evaluate the performance of the proposed system. Our novel method achieves higher accuracy compared to previous literature, with the added benefit of low latency for inference.

中文翻译：

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使用优化的活动区域进行面部表情识别

在本文中，我们报告了一种有效的面部表情识别系统，可以准确地对六个或七个基本表情进行分类。代替使用整个面部区域，我们定义了三种活动区域，即左眼区域，右眼区域和嘴巴区域。我们提出了一种从三种活动区域中搜索优化的活动区域的方法。卷积神经网络（CNN）针对每种优化的活动区域进行训练，以提取特征并分类表达式。为了获得可表示的特征，对表情图像进行直方图均衡，旋转校正和空间归一化。应用决策级融合方法，通过该方法，通过三个CNN结果的多数表决获得表情识别的最终结果。在独立数据库和融合数据库上均进行了实验，以评估所提出系统的性能。与以前的文献相比，我们的新颖方法具有更高的准确性，并具有低延迟推理的优势。