Emotion Detection in Videos Using Non Sequential Deep Convolutional Neural Network

Emotions are fundamental for humans. They affect perception and everyday activities such as communication, learning and decision-making. Facial expression and body language are the main sources of this information. The goal is to classify these emotions to improve human-computer interaction. In proposed method, a non-sequential deep convolutional neural network is presented. It consists of multiple networks which run in parallel. These parallel networks are then merged together followed by relu, max-pool, drop-out, dense and soft-max layers. In proposed model, we have used multiple networks to cover local and global feature. The evaluation of proposed method is done by using Surrey Audio-Visual Expressed Emotion (SAVEE) dataset containing four persons' covering seven emotions' in their videos. The results show the validity of proposed system.