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基于知识迁移的旅客人脸图像异常检测方法研究

朱宇豪, 李平, 戴琳琳, 景辉, 董兴芝

朱宇豪, 李平, 戴琳琳, 景辉, 董兴芝. 基于知识迁移的旅客人脸图像异常检测方法研究[J]. 铁路计算机应用, 2023, 32(6): 7-13. DOI: 10.3969/j.issn.1005-8451.2023.06.02
引用本文: 朱宇豪, 李平, 戴琳琳, 景辉, 董兴芝. 基于知识迁移的旅客人脸图像异常检测方法研究[J]. 铁路计算机应用, 2023, 32(6): 7-13. DOI: 10.3969/j.issn.1005-8451.2023.06.02
ZHU Yuhao, LI Ping, DAI Linlin, JING Hui, DONG Xingzhi. Visual anomaly detection method for passenger facial images based on knowledge transferring[J]. Railway Computer Application, 2023, 32(6): 7-13. DOI: 10.3969/j.issn.1005-8451.2023.06.02
Citation: ZHU Yuhao, LI Ping, DAI Linlin, JING Hui, DONG Xingzhi. Visual anomaly detection method for passenger facial images based on knowledge transferring[J]. Railway Computer Application, 2023, 32(6): 7-13. DOI: 10.3969/j.issn.1005-8451.2023.06.02

基于知识迁移的旅客人脸图像异常检测方法研究

基金项目: 北京经纬信息技术有限公司科研项目(DZYF22-06)
详细信息
    作者简介:

    朱宇豪,在读博士研究生

    李 平,首席研究员

  • 中图分类号: U293.2 : TP391.4

Visual anomaly detection method for passenger facial images based on knowledge transferring

  • 摘要: 基于深度学习的人脸识别技术以数据为驱动,对输入图像的质量要求较高。在铁路刷脸进/出站场景下,为滤除因各种因素导致的成像异常的人脸图像,提升人脸识别精度,文章研究人脸图像正常的特征分布,通过知识迁移,提出无须针对异常样本建模的人脸图像异常检测算法。理想情况下,该算法对人脸图像异常检测的ROC曲线下面积(AUROC,Aera Under Receiver Operating Characteristic)可达到0.979。实验结果表明,该算法在计算精度与运行成本的组合上具有较高的自由度,可实现不同场景、硬件条件下的算法适配,为优化旅客人脸识别的输入环节,提高各场景下的旅客人脸识别率提供了技术支撑。
    Abstract:
    As a data-driven technology, face recognition based on deep learning demands high-quality images as input.
    In order to filter out facal images with abnormalities caused by various factors in the scene of railway face recognition entering and exiting stations, and improve the accuracy of facial recognition, this paper studied the feature distribution of normal facial images, through knowledge transfer, and proposed a anomaly detection algorithm for facial images that did not require modeling abnormal samples. The AUROC of this algorithm for detecting facial images with abnormalities was 0.979. The experimental results show that the algorithm has a high degree of freedom in the combination of computational accuracy and operating costs, and can meet the algorithm adaptation in different scenarios and hardware conditions. It provides technical support for optimizing the input link of passenger face recognition and improving the passenger face recognition rate in various scenarios.
  • 图  1   人脸图像异常检测算法架构

    图  2   人脸分割模型(BiSeNet)架构

    图  3   特征提取与构建流程

    图  4   不同模型取不同特征百分比时人脸图像异常检测的ROC曲线

    输入:人脸正常的特征分布$\mathbfcal{D}$,保留特征的百分比r
    输出:特征选择后的子集$\mathbfcal{S}$
     $\mathbfcal{S}\leftarrow \left\{\right\}$  $for\;i\;in\;range\left(r\right|\mathbfcal{D}\left|\right):$   ${{\boldsymbol{m}}}_{i}\leftarrow{\mathrm{argmax}}_{{\boldsymbol{m}}\in \mathbfcal{D}-\mathbfcal{S}}\;\;\;\;\;{\mathrm{min}}_{n\in \mathbfcal{S}}{\left|\right|{\boldsymbol{m}}-{\boldsymbol{n}}\left|\right|}_{2}$  $\mathbfcal{S}\leftarrow \mathbfcal{S}\bigcup \left\{{{\boldsymbol{m}}}_{i}\right\}$ $ end $
    下载: 导出CSV

    表  1   人脸图像异常检测结果(有人脸分割)

    特征百分比 $ r $模型AUROC运行速度/fps
    1%ResNet-180.857约450
    ResNet-500.947
    ResNet-1000.979
    10%ResNet-180.882约50
    ResNet-500.950
    ResNet-1000.975
    25%ResNet-180.890约20
    ResNet-500.948
    ResNet-1000.971
    50%ResNet-180.893约10
    ResNet-500.944
    ResNet-1000.969
    下载: 导出CSV

    表  2   人脸图像异常检测结果(无人脸分割)

    特征百分比 $ r $模型AUROC运行速度/fps
    1%ResNet-180.898约450
    ResNet-500.924
    ResNet-1000.938
    10%ResNet-180.880约50
    ResNet-500.911
    ResNet-1000.929
    25%ResNet-180.866约20
    ResNet-500.904
    ResNet-1000.923
    50%ResNet-180.860约10
    ResNet-500.901
    ResNet-1000.921
    下载: 导出CSV
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  • 收稿日期:  2022-11-08
  • 刊出日期:  2023-06-24

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