Deep Learning Neural Network Architecture for Human Facial Expression Recognition

Facial Expression recognition (FER) is a vital field of artificial intelligence and computer vision owing to its remarkable academic potential. Recognition of facial expression has many implicit applications, and it has attracted much attention of researchers during the last decade. The relation between the facial features to each human emotion is the main focus of the investigation. By training a neural network with such features, one can realize a system that can provide analytical data on human behaviors. This paper proposes a facial expression recognition system architecture based on a deep convolutional neural network. In the course, various neural networks are studied and compared on their facial expression recognition, and one can find that the CNN deep learning method can be one of the best options to take. The paper includes human facial emotion detection procedures that include three significant steps: face recognition; characteristic feature extraction; emotion classification. A set of experiments is performed to evaluate the proposed system’s performance, and the recognition accuracy is measured in terms of the facial emotion recognition challenge (FERC-2013) and Japanese female facial expression (JAFFE) dataset.