Few-Shot Learning for Multi-POSE Face Recognition via Hypergraph De-Deflection and Multi-Task Collaborative Optimization

Few-shot, multi-pose face recognition has always been an interesting yet difficult subject in the field of pattern recognition. Researchers have come up with a variety of workarounds; however, these methods make it either difficult to extract effective features that are robust to poses or difficult to obtain globally optimal solutions. In this paper, we propose a few-shot, multi-pose face recognition method based on hypergraph de-deflection and multi-task collaborative optimization (HDMCO). In HDMCO, the hypergraph is embedded in a non-negative image decomposition to obtain images without pose deflection. Furthermore, a feature encoding method is proposed by considering the importance of samples and combining support vector data description, triangle coding, etc. This feature encoding method is used to extract features from pose-free images. Last but not the least, multi-tasks such as feature extraction and feature recognition are jointly optimized to obtain a solution closer to the global optimal solution. Comprehensive experimental results show that the proposed HDMCO achieves better recognition performance.