# Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

## TL;DR

This paper introduces Deep-Emotion, an attentional convolutional network for facial expression recognition that outperforms previous models by focusing on key facial regions and utilizing visualization techniques to interpret emotion-specific features.

## Contribution

The work presents a novel attentional convolutional network that enhances facial expression recognition accuracy and provides insights into emotion-related facial regions.

## Key findings

- Significant improvement over previous models on multiple datasets
- Different emotions are associated with distinct facial regions
- Visualization techniques reveal important face areas for emotion detection

## Abstract

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.