Depth Face Recognition Algorithm Based on Adaptive Circle Margin

doi:10.3969/j.issn.1000-1093.2021.11.016

Abstract

Abstract: Face recognition is an important research direction of computer vision，and the effective loss functions play a vital role in face recognition. In view of the fact that the existing loss function does not consider the marginal situation to result in a limited model convergence and a low generalization ability is for unbalanced samples，AdaCMloss (Adaptive circle Margin Loss) loss function method is proposed for studying the margin itself.Through the self-adaptive learning of the margin，the unique margin can be learnt for different categories，and the self-adaptive circle margin is generated. A more margin is learnt for a small number of samples，so that the intra-class compression of the data of a small number of samples is more compact and the model generalization ability is stronger. The common face recognition benchmarks Megaface, IJB-C, LFW, LFW BLUFR and YTF are extensively analyzed and experimentally varified. The results show that the proposed method is used to improve the convergence accuracy of existing methods by 0.5% in unbalanced data sets and enhance the model generalization ability effectively, and has a clear convergence state.

Key words: deepfacerecognition, adaptivecirclemargin, lossfunction, modelgeneralizationability, convergencestate

CLC Number:

TP391.41

CAI Hua， SUN Jun， ZHU Ruikun， ZHU Xinli， ZHAO Yiwu. Depth Face Recognition Algorithm Based on Adaptive Circle Margin[J]. Acta Armamentarii, 2021, 42(11): 2424-2432.

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