Face Recognition Method Based on Attention Mechanism and Curriculum Learning

Aiming at the problems that the facial features extracted from current face recognition algorithms are not distinguishable and the discrimination of difficult and easy samples is not enough, a face recognition algorithm combining attention mechanism and curriculum learning is proposed, which is called efficient cooperative attention and curriculum face (ECACFace). The algorithm proposes an efficient spatial channel attention module (ESCA) and integrates it into the basic module of the feature extraction network. The efficient channel attention module (ECA) is used to obtain the channel attention, and the spatial attention module is added after the ECA. On the basis of paying attention to the image channel information, the spatial attention is further obtained, and the face feature vector with richer information is obtained for face classification. At the same time, the loss function based on curriculum learning is introduced to distinguish the difficult and easy samples in the training process. The simple samples are trained in the early stage and the difficult samples are trained in the later stage to realize the discriminative sample learning. Training ECACFace based on lightweight network and shallow network on CASIA-WebFace dataset and it has an accuracy improvement of more than 1.5 percentage points compared with the original network. ECACFace based on deep network is trained on MS1MV2 dataset which has millions of data, and the accuracy tested on CPLFW dataset is increased by 1.14 percentage points compared with ArcFace. Experimental results show that the cooperation of ESCA module and the loss function based on curriculum learning can further improve the perfor-mance of face recognition.