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基于MobileFaceNet网络改进的人脸识别方法

张子昊 王蓉

张子昊, 王蓉. 基于MobileFaceNet网络改进的人脸识别方法[J]. 北京航空航天大学学报, 2020, 46(9): 1756-1762. doi: 10.13700/j.bh.1001-5965.2020.0049
引用本文: 张子昊, 王蓉. 基于MobileFaceNet网络改进的人脸识别方法[J]. 北京航空航天大学学报, 2020, 46(9): 1756-1762. doi: 10.13700/j.bh.1001-5965.2020.0049
ZHANG Zihao, WANG Rong. Improved face recognition method based on MobileFaceNet network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1756-1762. doi: 10.13700/j.bh.1001-5965.2020.0049(in Chinese)
Citation: ZHANG Zihao, WANG Rong. Improved face recognition method based on MobileFaceNet network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1756-1762. doi: 10.13700/j.bh.1001-5965.2020.0049(in Chinese)

基于MobileFaceNet网络改进的人脸识别方法

doi: 10.13700/j.bh.1001-5965.2020.0049
基金项目: 

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

详细信息
    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

    通讯作者:

    王蓉, E-mail:dbdxwangrong@163.com

  • 中图分类号: O235;TP183

Improved face recognition method based on MobileFaceNet network

Funds: 

Ministry of Public Security Technology Research Project 2017JSYJB01

the Fundamental Research Funds for the Central Universities 2019JKF111

More Information
  • 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。

     

  • 图 1  可分离卷积示意图

    Figure 1.  Schematic diagram of detachable convolution

    图 2  人脸识别方法流程

    Figure 2.  Face recognition method flowchart

    图 3  SRM模块结构

    Figure 3.  SRM module structure

    图 4  MobileFaceNet中bottleneck层结构

    Figure 4.  Structure of bottleneck layer in MobileFaceNet

    图 5  MobileFaceNet-SRM bottleneck层结构

    Figure 5.  MobileFaceNet-SRM bottleneck layer structure

    图 6  三种主干网络在AgeDB数据集上的ROC曲线

    Figure 6.  ROC curves of three backbone networks on AgeDB dataset

    表  1  MobileNetV2网络结构

    Table  1.   MobileNetV2 network structure

    输入 卷积操作 z c n v
    224×224×3 Conv2d 32 1 2
    112×112×32 bottleneck 1 16 1 1
    112×112×16 bottleneck 6 24 2 2
    56×56×24 bottleneck 6 32 3 2
    28×28×32 bottleneck 6 64 4 2
    28×28×64 bottleneck 6 96 3 1
    14×14×96 bottleneck 6 160 3 2
    7×7×160 bottleneck 6 320 1 1
    7×7×320 Conv2d 1×1 1280 1 1
    7×7×1280 Avgpool 7×7 1
    1×1×f Conv2d 1×1 f
    下载: 导出CSV

    表  2  MobileFaceNet网络结构

    Table  2.   MobileFaceNet network structure

    输入 卷积操作 z c n v
    112×112×3 Conv 3×3 64 1 2
    56×56×64 Depthwise Conv 3×3 64 1 1
    56×56×64 bottleneck 2 64 5 2
    28×28×64 bottleneck 4 128 1 2
    14×14×128 bottleneck 2 128 6 1
    14×14×128 bottleneck 4 128 1 2
    7×7×128 bottleneck 2 128 2 1
    7×7×128 Conv 1×1 512 1 1
    7×7×512 Linear GDConv 7×7 512 1 1
    1×1×512 Linear Conv 1×1 128 1 1
    下载: 导出CSV

    表  3  基于AdaCos的损失函数不同卷积框架人脸识别模型性能比较

    Table  3.   Performance comparison of face recognition models with different convolution frames based on AdaCos loss function

    网络结构 测试准确度/% 模型大小/MB
    LFW CFP-FF AgeDB
    ResNet50 98.41 97.88 88.46 161.5
    MobileFaceNet 98.90 98.69 90.94 4.9
    MobileFaceNet-SRM 99.15 98.85 91.24 5.2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-02-25
  • 录用日期:  2020-04-18
  • 刊出日期:  2020-09-20

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