Face Detection and Emotion Classification Using Single Deep Convolutional Network for Facial Emotion Recognition

Abstract

In this paper, we propose a facial expression recognition system using a deep convolutional network. Previous works used the facial action coding system (FACS) to classify emotions. Therefore, the system consists of a face detector, a feature extractor, a facial action classifier, and an emotional state classifier in series. In contrast, the proposed system is a simplified emotion recognition system that performs face detection and emotion classification in parallel. Moreover, the model was trained without any prior knowledge of FACS. We evaluated its performance on four different databases. Our main contributions are two folds: 1) Our simplified facial expression recognition system processes images in real-time. 2) Our model was trained to classify facial expressions without any action unit (AU) related information. The proposed method achieved a classification accuracy of 98.6% on six basic emotions and a neutral state from faces with five different angles. The experimental results showed that the deep convolutional network could classify emotional states from a multi-angle facial expressions database and various facial expression databases without the use of hand-crafted features. © ICROS 2019.