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。
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.
作者
朱宇豪
李平
戴琳琳
景辉
董兴芝
ZHU Yuhao;LI Ping;DAI Linlin;JING Hui;DONG Xingzhi(Postgraduate Department,China Academy of Railway Sciences,Beijing 100081,China;China Academy of Railway Sciences Corporation Limited,Beijing 100081,China;Institute of Computing Technologies,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China)
出处
《铁路计算机应用》
2023年第6期7-13,共7页
Railway Computer Application
基金
北京经纬信息技术有限公司科研项目(DZYF22-06)。
关键词
人脸预处理
图像质量评估
图像异常检测
预训练模型
知识迁移
算法适配
face pre-processing
image quality assessment
visual anomaly detection
pre-trained model
knowledge transfering
algorithm adaptation.
