角度空间三元组损失微调的人脸识别

任克强; 胡慧

doi:10.3788/YJYXS20193401.0110

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角度空间三元组损失微调的人脸识别

图像处理 | 更新时间：2020-07-05

- 角度空间三元组损失微调的人脸识别
- Face recognition of triple loss fine-tuning in angular space
- 液晶与显示 2019年34卷第1期页码：110-117
- 作者机构：
  
  江西理工大学信息工程学院, 江西赣州 341000
- 作者简介：
  
  [ "任克强(1959-), 男, 湖南汨罗人, 硕士, 教授, 1993年于华中理工大学获得硕士学位, 主要从事图像与视频处理、信息隐藏方面的研究。E-mail:jxrenkeqiang@163.com" ]
- 基金信息：
  
  国家自然科学基金(61501210);Supported by National Natural Science Foundation of China(61501210)
- DOI：10.3788/YJYXS20193401.0110
  中图分类号： TP391
- 收稿日期：2018-07-18，
  
  录用日期：2018-10-19，
  
  纸质出版日期：2019-02
- 稿件说明：
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任克强, 胡慧. 角度空间三元组损失微调的人脸识别[J]. 液晶与显示, 2019,34(1):110-117.

Ke-qiang REN, Hui HU. Face recognition of triple loss fine-tuning in angular space[J]. Chinese journal of liquid crystals and displays, 2019, 34(1): 110-117.
任克强, 胡慧. 角度空间三元组损失微调的人脸识别[J]. 液晶与显示, 2019,34(1):110-117. DOI： 10.3788/YJYXS20193401.0110.

Ke-qiang REN, Hui HU. Face recognition of triple loss fine-tuning in angular space[J]. Chinese journal of liquid crystals and displays, 2019, 34(1): 110-117. DOI： 10.3788/YJYXS20193401.0110.

摘要

针对角度Softmax损失强约束存在的问题，提出一种用角度空间三元组损失对角度Softmax损失预训练模型进行微调的算法。算法首先对原来的卷积神经网络结构进行改进，将1×1卷积核与池化层加在不同残差块间，以进行选择更有效的特征。然后用角度空间下的三元组损失对预训练模型进行微调，以降低困难样本的强约束条件。最后在测试时，分别提取原始人脸图像特征和水平翻转的人脸图像特征，对两个特征相加作为最终的人脸特征表达，以丰富人脸特征信息，提高识别率。实验结果表明，在LFW和YTF人脸数据集分别取得了99.25%和94.52%的识别率，在大规模人脸身份识别中，本文提出的方法在仅用单模型和比较小的训练集就能有效地提高人脸识别率。

Abstract

Aiming at the problem that angular Softmax loss exists in strong constraint

an algorithm to fine-turn the angular Softmax loss pre-training model by using triple loss in angle space was proposed in this paper. Firstly

the original structure of convolution neural network was improved and the convolution kernels of 1×1 and pooling layers were added between different residual blocks to select more effective features. Secondly

the pre-training model was fine-tuned by triple loss in angular space to reduce the strong constraint condition of difficult samples. Finally

in the test

the features of original and the horizontal flipped face images were extracted respectively

and the two features were added as the final facial feature expression

so as to enrich the face feature information and improve the recognition rate. The experimental results show that the recognition rates of the LFW and YTF face data sets are 99.25% and 94.52%

respectively. The method proposed in this paper can effectively improve the face recognition rate in large-scale face identification by using only a single model and a relatively small training set.

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Keywords

references

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