计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 71-77.doi: 10.11896/jsjkx.210300222
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
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。
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