基于残差网络与中心损失的人脸识别
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摘要
针对常规深度学习的人脸识别方法训练数据量巨大和训练难收敛的问题,提出一种基于残差网络与中心损失的人脸识别方法。利用生成对抗网络方法解决训练数据分布不均衡问题,利用数据增强方法解决数据不足问题;改进残差网络,使其匹配较小数据集,解决训练难收敛问题;将交叉熵损失与中心损失结合,作为模型训练过程中的监督信号,使类间分散、类内聚合。实验结果表明,在小数据的前提下,识别算法能够准确地识别出人脸。模型在测试集上的准确率达97.46%。
Existing face recognition algorithms based on deep learning algorithms are difficult to train because they require huge amount of training dataset.A method based on Resnet and center loss was proposed.The unbalanced distribution of data was resolved using generative adversarial nets and the insufficiency of data was solved by data augmentation.Resnet was improved for small dataset and the difficult problem of training convergence was resolved.The cross-entropy loss and center loss were combined as the supervisory signal in the model training that contributed to inter-class dispersion and intra-class aggregation.With the limit of small dataset,experimental results show that the testing-accuracy rate achieves 97.46%.
Existing face recognition algorithms based on deep learning algorithms are difficult to train because they require huge amount of training dataset.A method based on Resnet and center loss was proposed.The unbalanced distribution of data was resolved using generative adversarial nets and the insufficiency of data was solved by data augmentation.Resnet was improved for small dataset and the difficult problem of training convergence was resolved.The cross-entropy loss and center loss were combined as the supervisory signal in the model training that contributed to inter-class dispersion and intra-class aggregation.With the limit of small dataset,experimental results show that the testing-accuracy rate achieves 97.46%.
引文
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[10]Ioffe S,Szegedy C.Batch normalization:Accelerating deep network training by reducing internal covariate shift[C]//International Conference on International Conference on Machine Learning.JMLR.org,2015:448-456.
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[13]Goodfellow I J,Pouget-Abadie J,Mirza M,et al.Generative adversarial nets[C]//International Conference on Neural Information Processing Systems.MIT Press,2014:2672-2680.
[14]Li Y,Xiao N,Ouyang W.Improved boundary equilibrium generative adversarial networks[J].IEEE Access,2018(6):11342-11348.