Automatic facial expression recognition (FER) is an important technique in human–computer interfaces and surveillance systems. It classifies the input facial image into one of the basic expressions (anger, sadness, surprise, happiness, disgust, fear, and neutral). There are two types of FER algorithms: feature-based and convolutional neural network (CNN)-based algorithms. The CNN is a powerful classifier, however, without proper auxiliary techniques, its performance may be limited. In this study, we improve the CNN-based FER system by utilizing face frontalization and the hierarchical architecture. The frontalization algorithm aligns the face by in-plane or out-of-plane, rotation, landmark point matching, and removing background noise. The proposed adaptive exponentially weighted average ensemble rule can determine the optimal weight according to the accuracy of classifiers to improve robustness. Experiments on several popular databases are performed and the results show that the proposed system has a very high accuracy and outperforms state-of-the-art FER systems.

自动面部表情识别（FER）是人机界面和监视系统中的一项重要技术。它将输入的面部图像分类为基本表达之一（愤怒，悲伤，惊奇，幸福，厌恶，恐惧和中立）。FER算法有两种类型：基于特征的算法和基于卷积神经网络（CNN）的算法。CNN是强大的分类器，但是，如果没有适当的辅助技术，其性能可能会受到限制。在这项研究中，我们通过利用人脸正面化和分层体系结构来改进基于CNN的FER系统。正面化算法通过平面内或平面外，旋转，界标点匹配以及消除背景噪声来对齐人脸。提出的自适应指数加权平均集成规则可以根据分类器的精度确定最优权重，以提高鲁棒性。在几个流行的数据库上进行了实验，结果表明所提出的系统具有很高的准确性，并且优于最新的FER系统。