Deep Convolutional Neural Network for Facial Expression Recognition using Facial Parts

This paper proposes the design of a Facial Expression Recognition (FER) system based on deep convolutional neural network by using facial parts. In this work, a simple solution for facial expression recognition that uses a combination of algorithms for face detection, feature extraction and classification is discussed. The proposed method uses a two-channel convolutional neural network in which Facial Parts (FPs) are used as input to the first convolutional layer, the extracted eyes are used as input to the first channel while the mouth is the input into the second channel. Information from both channels converges in a fully connected layer which is used to learn global information from these local features and is then used for classification. Experiments are carried out on the Japanese Female Facial Expression (JAFFE) and the Extended Cohn-Kanada (CK+) datasets to determine the recognition accuracy for the proposed FER system. The results achieved shows that the system provides improved classification accuracy when compared to other methods.