Automatic Facial Expression Recognition Method Using Deep Convolutional Neural Network

Abstract

Facial expressions are part of human language and are often used to convey emotions. Since humans are very different in their emotional representation through various media, the recognition of facial expression becomes a challenging problem in machine learning methods. Emotion and sentiment analysis also have become new trends in social media. Deep Convolutional Neural Network (DCNN) is one of the newest learning methods in recent years that model a human's brain. DCNN achieves better accuracy with big data such as images. In this paper an automatic facial expression recognition (FER) method using the deep convolutional neural network is proposed. In this work, a way is provided to overcome the overfitting problem in training the deep convolutional neural network for FER, and also an effective pre-processing phase is proposed that is improved the accuracy of facial expression recognition. Here the results for recognition of seven emotional states (neutral, happiness, sadness, surprise, anger, fear, disgust) have been presented by applying the proposed method on the two largely used public datasets (JAFFE and CK+). The results show that in the proposed method, the accuracy of the FER is better than traditional FER methods and is about 98.59% and 96.89% for JAFFE and CK+ datasets, respectively.