摘要
人脸识别被广泛应用于购物、安检、出行、支付和考勤等日常生活中,人脸识别系统需要大的算力与存储空间,因此往往将需要识别的人脸通过网络传送到云平台进行识别,但网络覆盖、拥塞或延时等问题造成人脸识别系统难以满足实际应用的需求,用户体验差。针对人脸识别中存在的问题,提出了基于边云协同的人脸识别方法。该方法结合云计算的处理能力和边缘计算的实时性,使人脸识别系统不受网络状态的约束,应用更加广泛,用户体验更好。在云端,提出了LResNet特征提取方法,改进了ResNet34网络结构,并利用ArcFace人脸损失函数监督训练过程,使网络学习到更多的人脸角度特性;在边缘端,针对计算资源和存储资源有限的问题,提出了SResNet特征提取方法,利用深度可分离卷积轻量化LResNet网络结构,大大减少了网络参数和计算量。边云协同的人脸识别实验表明,所提系统在任何网络状态下都能进行实时识别且准确率较高。
Face recognition is widely used in daily life such as shopping,security check,travel,payment and work attendance.Face recognition systems need strong computing power and large storage space,so face images that need to be recognized are usually transmitted to the cloud platform through the network.Due to the problems of network coverage,congestion and delay,face re-cognition systems are difficult to meet the needs of actual application,and the user experience is poor.Aiming at the problems in face recognition,a face recognition method based on edge-cloud collaboration is proposed.This method combines the processing ability of cloud computing and the real-time performance of edge computing,so that face recognition systems are not constrained by the network status,and its application is more extensive and the user experience is better.In the cloud,the LResNet feature extraction method is proposed to improve the ResNet34 network structure,and the ArcFace face loss function is used to supervise the training process,so that the network can learn more face angle features.At the edge,due to the limited computing resources and storage resources,a SResNet feature extraction method is proposed.Deep separable convolution is used to lighten the LResNet network structure,and it has greatly reduced network parameters and computation.The face recognition experiment on edge-cloud collaboration shows that the system can recognize faces in real time with a high accuracy rate under any network status.
作者
魏勤
李瑛娇
娄平
严俊伟
胡辑伟
WEI Qin;LI Ying-jiao;LOU Ping;YAN Jun-wei;HU Ji-wei(School of Information Engineering,Wuhan University of Technology,Wuhan 430070,China)
出处
《计算机科学》
CSCD
北大核心
2022年第5期71-77,共7页
Computer Science
基金
国家自然科学基金(52075404)
武汉市科技局应用基础前沿专项(2020010601012176)。
作者简介
通讯作者:魏勤,born in 1980,Ph.D,associate professor,postgraduate supervisor.Her main research interests include signal processing and fault diagnosis,qinwei@whut.edu.cn。
