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Dynamic Facial Expression Recognition Based on Deep Learning

With the development of deep learning, CNN model has performed much better than traditional machine learning algorithm in many computer visual recognition tasks. In the field of Facial Expression Recognition (FER), although there are many recognition systems using DNNs, their accuracy and practicality are still not suitable for practical applications. This paper proposed a 3D Convolutional Neural Network method for FER in videos. This neural network consists of a Stem layer, a 3D Inception-ResNets structure, a GRU layer, a Dropout layer, an Island layer and a Softmax layer, which can capture spatial relationships in facial expression images and temporal relationships among different face frames. In order to increase the contribution degree of the important face component in the expression recognition, important feature point information of the facial expression was extracted and input into the network structure. In addition, a new Island loss function with strong ability to distinguish different features is introduced. It can not only reduce the intra-class variations, but also enlarge the inter-class differences, which could better ignore the effects of facial expression variability and sensitivity, and achieve higher recognition accuracy and generalization ability. The proposed method was evaluated for using three public available databases in subject-independent and cross-database tasks and outperformed the state-of-the-art methods.