中圖分類號(hào)： TN911.73；TP391.4 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.200968 中文引用格式： 況朝青，賀超，王均成，等. 基于邊緣計(jì)算中極端姿態(tài)和表情的人臉識(shí)別[J].電子技術(shù)應(yīng)用，2021，47(6)：30-34. 英文引用格式： Kuang Chaoqing，He Chao，Wang Juncheng，et al. Face recognition with extreme posture and expression[J]. Application of Electronic Technique，2021，47(6)：30-34.
Face recognition with extreme posture and expression
1.School of Communication and Information Engineering，Chongqing University of Posts and Telecommunications， Chongqing 400065，China； 2.Optical Communications and Networks Key Laboratory of Chongqing，Chongqing 400065，China； 3.Ubiquitous Sensing and Networking Key Laboratory of Chongqing，Chongqing 400065，China
Abstract： With the development of information technology, face recognition is used more and more in payment, work and security system. In the edge computing system, in order to deal with the speed, we usually choose a smaller neural network for face recognition, which may cause the recognition rate is not very high. And in practical applications, most of them can recognize the face with high image quality, but the recognition rate is not very high for the face which is greatly affected by the light and has great changes in expression and posture. Therefore, this paper chooses the SqueezeNet lightweight network, which has a small number of layers and can be well used in edge computing system. The method of preprocessing is used to preprocess the image, and then the loss function of SqueezeNet network and the residual learning method of ResNet network are improved. Finally, through the test of LFW and IJB-A data set, it is concluded that the research method in this paper can significantly improve the recognition rate.
Key words : neural network；face recognition；preprocessing；SqueezeNet network；ResNet network
