北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (9): 1756-1762.doi: 10.13700/j.bh.1001-5965.2020.0049

• 论文 • 上一篇    下一篇

基于MobileFaceNet网络改进的人脸识别方法

张子昊1,2, 王蓉1,2   

  1. 1. 中国人民公安大学 警务信息工程与网络安全学院, 北京 100038;
    2. 安全防范技术与风险评估公安部重点实验室, 北京 100038
  • 收稿日期:2020-02-25 发布日期:2020-09-22
  • 通讯作者: 王蓉 E-mail:dbdxwangrong@163.com
  • 作者简介:张子昊 男,硕士研究生。主要研究方向:行人再识别;王蓉 女,博士,教授,博士生导师。主要研究方向:模式识别、人工智能。
  • 基金资助:
    公安部技术研究计划项目(2017JSYJB01);中央高校基本科研业务费专项资金(2019JKF111)

Improved face recognition method based on MobileFaceNet network

ZHANG Zihao1,2, WANG Rong1,2   

  1. 1. School of Police Information Engineering and Network Security, People's Public Security University of China, Beijing 100038, China;
    2. Key Laboratory of Security Technology&Risk Assessment, Beijing 100038, China
  • Received:2020-02-25 Published:2020-09-22
  • Supported by:
    Ministry of Public Security Technology Research Project (2017JSYJB01); the Fundamental Research Funds for the Central Universities (2019JKF111)

摘要: 为了解决训练过程中卷积模型参数较多、收敛速度较慢的问题,提出了一种基于MobileFaceNet网络改进的人脸识别方法。首先,使用MobileFaceNet网络提取人脸特征,在提取特征的过程中,通过引入可分离卷积减少模型中卷积层参数的数量;其次,通过在MobileFaceNet网络中引入风格注意力机制来增强特征的表达,同时使用AdaCos人脸损失函数来训练模型,利用AdaCos损失函数中的自适应缩放系数,来动态地调整超参数,避免了人为设置超参数对模型的影响;最后,分别在LFW、AgeDB和CFP-FF测试数据集上对训练模型进行评估。实验结果显示:改进后的模型在LFW、AgeDB和CFP-FF测试数据集上的识别精度分别提升了0.25%、0.16%和0.3%,表明改进后的模型相较于改进前的模型在精度和鲁棒性上有所提高。

关键词: 人脸识别, 深度学习, MobileFaceNet, AdaCos, 卷积神经网络

Abstract: In order to solve the problem of more convolutional model parameters and slower convergence speed during training, an improved face recognition method based on MobileFaceNet network is proposed. First, we use the MobileFaceNet network to extract facial features. In the process of extracting features, the number of convolutional layer parameters in the model is reduced by introducing separable convolution. Then, the style attention mechanism is introduced in the MobileFaceNet network to enhance the expression of features. At the same time, the AdaCos face loss function is used to train the model, and the adaptive scaling factor in the AdaCos loss function is used to dynamically adjust the hyperparameters to avoid the effect of artificially setting hyperparameters on the model. Finally, we evaluate the training model on the LFW, AgeDB and CFP-FF test dataset, respectively. The experimental results show that the recognition accuracy of the improved model on the LFW, AgeDB and CFP-FF test dataset has increased by 0.25%, 0.16% and 0.3%, respectively, indicating that the improved model has higher accuracy and robustness than the model before improvement.

Key words: face recognition, deep learning, MobileFaceNet, AdaCos, convolutional neural network

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