基于自适应圆边际的深度人脸识别算法

人脸识别是计算机视觉的一个重要研究方向，其中有效的损失函数在人脸识别中起着至关重要的作用。针对现有损失函数没有考虑边际情况，导致模型收敛有限，且在不均衡样本中泛化能力不强的问题，提出自适应圆边际损失函数方法，对边际自身进行研究。通过对边际进行自适应学习，为不同类别学习独有的边际，产生自适应圆边际。为少量样本学习更大边际，从而对少量样本数据类内压缩更紧凑，使模型泛化能力更强，对5种常见的人脸识别基准Megaface、IJB-C、LFW、LFW BLUFR和YTF进行广泛分析和实验验证。结果表明，该方法在不均衡数据集中对现有方法的精确度整体提高了0.5%，有效提高了模型泛化能力，具有明确的收敛状态。

Depth Face Recognition Algorithm Based on Adaptive Circle Margin

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.