The design of the loss function is crucial in deep face recognition.A common practice is to add a fixed margin term to all classes to modify the decision boundary between classes,compress the distance between intra-class fea-tures,and improve the ability of the model to separate features of different classes.However,adding the same margin term for all classes may ignore the inconsistency between classes in the face recognition dataset.In order to further improve the effectiveness of the model,we argue that the model should pay different attention to the samples of different classes accord-ing to the learning difficulty of the class.In this paper,we introduce a method for hard class mining based on the bias be-tween the center of the class mean and the center of the class weight,called center bias estimation.The method proposed in this paper adaptively assigns margin terms of different sizes to different classes according to the value of center bias estima-tion.At the same time,to solve the problem of unstable calculation of center bias estimation in the early stage of training,we propose an adaptively changing convergence parameter to adjust the credibility of center bias estimation and design rele-vant experiments to prove the effectiveness of the convergence parameters.In the face verification baseline dataset,the pro-posed method in this paper is improved by 0.26％on average accuracy compared with the baseline method,reaching 96.62％.In two large face verification test datasets,when FPR is equal to 0.01％,the TPR scores of our method is improved by 0.58％and 0.22％,respectively,and the experimental results of 88.47％and 92.29％are obtained,and multiple experi-mental results show that our method is better than the general existing algorithms.The implementation code is published on https://github.com/TCCofWANG/FR-Centers-Bias.
关键词
深度人脸识别/困难类别挖掘/类别不平衡/中心偏差估计/自适应间隔
Key words
deep face recognition/hard class mining/class imbalance/center bias estimation/adaptive margin
维普
万方数据