Facial expressions play an important role in conveying the emotional states
of human beings. Recently, deep learning approaches have been applied to image
recognition field due to the discriminative power of Convolutional Neural
Network (CNN). In this paper, we first propose a novel Multi-Region Ensemble
CNN (MRE-CNN) framework for facial expression recognition, which aims to
enhance the learning power of CNN models by capturing both the global and the
local features from multiple human face sub-regions. Second, the weighted
prediction scores from each sub-network are aggregated to produce the final
prediction of high accuracy. Third, we investigate the effects of different
sub-regions of the whole face on facial expression recognition. Our proposed
method is evaluated based on two well-known publicly available facial
expression databases: AFEW 7.0 and RAF-DB, and has been shown to achieve the
state-of-the-art recognition accuracy.

提出了一种基于 Multi-Region Ensemble CNN (MRE-CNN) 框架的面部表情识别方法，结合深度学习方法和 CNN，同时捕捉全局和局部特征确定面部表情，经过实验证明，取得了最新的识别精度。