| Abstract |
Background and objective:Face images often change in posture, Angle, expression, makeup, aging, lighting, and other aspects. The performance of many models with high accuracy in restricted scenes decreases sharply in unrestricted scenes. Therefore, the study of face recognition in unrestricted scenes is of great significance. In this paper, we proposed a face recognition algorithm based on depth map transfer learning to effectively recognize face images taken in unrestricted environment. Methods: Firstly, the deep network encoding and decoding structure based on dense feature fusion is used to extract depth information from face images. Secondly, depth information for migration learning is fused to improve the feature dimension of small sample data, thereby improving the problem of insufficient feature extraction training due to a small number of samples. Finally, in order to improve the classifier recognition performance in small sample machine learning, central loss is introduced into the traditional Softmax loss to improve the punishment supervision ability of the classification loss function. |
