Video-based face recognition based on deep convolutional neural network

With the rise of artificial intelligence in recent years, the field of object recognition is making rapid progress. Face recognition is a major subarea of object recognition which has already played a significant role in our life. However, despite the extensive study on the field of face recognition, video-based face recognition is still a tough area which needs further research. In this paper, we propose a model based on deep convolutional network for video-based face recognition. Our model split video images into two sets, a set of key frames and the other set is made up with non-keys, for different tasks to lower the computational complexity of the model. Besides, we introduce spatial pyramid pooling and center loss to our method for classification task. Our method presented in this paper reached an accuracy of 96.06% on YouTube Faces dataset. The results indicate our approach possesses high precision as well as a strong real-time performance.