Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

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