基于深度超分辨率重建的监控图像人脸识别  

Face recognition of monitored image based on deep super-resolution reconstruction

作　　者：朱海 王国中[1] 范涛[1] 杨露 Zhu Hai;Wang Guozhong;Fan Tao;Yang Lu(School of Communication and Information Engineering,Shanghai University,Shanghai 200444,China)

机构地区：[1]上海大学通信与信息工程学院

出　　处：《电子测量技术》2018年第16期61-64,共4页Electronic Measurement Technology

基　　金：国家863项目(2015AA015903)资助

摘　　要：针对当前视频监控中人脸分辨率低、识别难度大的问题,提出一种深度超分辨率的监控图像人脸识别框架。通过级联2个深度卷积神经网络模块,首先对获取的低分辨率人脸图像进行超分辨率重建,其次利用第2个卷积神将网络模块提取重构后的高分辨率人脸特征,提出一种结构优化的深度卷积神经网络,并将深度卷积神经网络学习到的特征送入SOFTMAX对人脸进行分类识别。在FERET数据集上进行验证,实验结果显示,研究提出的框架与传统方法相比,大幅提高低分辨率情况下的监控图像人脸识别精度。In view of the low resolution and difficulty of recognition in current video surveillance, a deep superresolution framework for monitoring image face recognition is proposed. By cascading two convolutional neural network module, the low resolution face image acquisition of super-resolution reconstruction, followed by the second convolution God network module extraction from high resolution facial feature reconstruction, finally using SOFTMAX for face classification and recognition. Experimental results on the FERET dataset show that the proposed framework can significantly improve the accuracy of face recognition in low resolution situations compared with traditional methods.

关 键 词：深度卷积网络级联 超分辨率重构 监控图像 人脸识别 

分 类 号：TN949.6[电子电信—信号与信息处理]