Facial expression recognition has been an active research area over the past
few decades, and it is still challenging due to the high intra-class variation.
Traditional approaches for this problem rely on hand-crafted features such as
SIFT, HOG and LBP, followed by a classifier trained on a database of images or
videos.
Most of these works perform reasonably well on datasets of images captured in
a controlled condition, but fail to perform as good on more challenging
datasets with more image variation and partial faces.
In recent years, several works proposed an end-to-end framework for facial
expression recognition, using deep learning models.
Despite the better performance of these works, there still seems to be a
great room for improvement.
In this work, we propose a deep learning approach based on attentional
convolutional network, which is able to focus on important parts of the face,
and achieves significant improvement over previous models on multiple datasets,
including FER-2013, CK+, FERG, and JAFFE.
We also use a visualization technique which is able to find important face
regions for detecting different emotions, based on the classifier's output.
Through experimental results, we show that different emotions seems to be
sensitive to different parts of the face.

该文介绍了针对面部表情识别问题，提出了一种基于注意力卷积网络的深度学习方法，并采用可视化技术确定了识别不同情绪对应的重要面部区域，相比先前的模型在多个数据集上都取得了明显的改进。