融合自注意力机制的生成对抗网络跨视角步态识别  

The cross-view gait recognition analysis based on generative adversarial networks derived of self-attention mechanism

作　　者：张红颖 包雯静 Zhang Hongying;Bao Wenjing(Tianjin Key Laboratory of Advanced Signal and Image Processing,Civil Aviation University of China,Tianjin 300300,China;College of Electronic Information and Automation,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学天津市智能信号与图像处理重点实验室,天津300300 [2]中国民航大学电子信息与自动化学院,天津300300

出　　处：《中国图象图形学报》2022年第4期1097-1109,共13页Journal of Image and Graphics

基　　金：国家重点研发计划资助(2018YFB1601200);中国民航大学天津市智能信号与图像处理重点实验室开放基金资助项目(2019ASP-TJ06)。

摘　　要：目的针对目前基于生成式的步态识别方法采用特定视角的步态模板转换、识别率随视角跨度增大而不断下降的问题,本文提出融合自注意力机制的生成对抗网络的跨视角步态识别方法。方法该方法的网络结构由生成器、视角判别器和身份保持器构成,建立可实现任意视角间步态转换的网络模型。生成网络采用编码器—解码器结构将输入的步态特征和视角指示器连接,进而实现不同视角域的转换,并通过对抗训练和像素级损失使生成的目标视角步态模板与真实的步态模板相似。在判别网络中,利用视角判别器来约束生成视角与目标视角相一致,并使用联合困难三元组损失的身份保持器以最大化保留输入模板的身份信息。同时,在生成网络和判别网络中加入自注意力机制,以捕捉特征的全局依赖关系,从而提高生成图像的质量,并引入谱规范化使网络稳定训练。结果在CASIA-B(Chinese Academy of Sciences’Institute of Automation gait database——dataset B)和OU-MVLP(OU-ISIR gait database-multi-view large population dataset)数据集上进行实验,当引入自注意力模块和身份保留损失训练网络时,在CASIA-B数据集上的识别率有显著提升,平均rank-1准确率比Gait GAN(gait generative adversarial network)方法高15%。所提方法在OU-MVLP大规模的跨视角步态数据库中仍具有较好的适用性,可以达到65.9%的平均识别精度。结论本文方法提升了生成步态模板的质量,提取的视角不变特征更具判别力,识别精度较现有方法有一定提升,能较好地解决跨视角步态识别问题。Objective Gait is a sort of human behavioral biometric feature,which is clarified as a style of person walks.Compared with other biometric features like human face,fingerprint and iris,the feature of gait is that it can be captured at a long-distance without the cooperation of the subjects.Gait recognition has its potential in surveillance security,criminal investigation and medical diagnosis.However,gait recognition is changed clearly in the context of clothing,carrying status,view variation and other factors,resulting in strong intra gradient changes in the extracted gait features.The relevant view change is a challenging issue as appearance differences are introduced for different views,which leads to the significant decline of cross view recognition performance.The existing generative gait recognition methods focus on transforming gait templates to a specific view,which may decline the recognition rate in a large variation of multi-views.A cross-view gait recognition analysis is demonstrated based on generative adversarial networks(GANs)derived of self-attention mechanism.Method Our network structure analysis is composed of generatorG,view discriminatorDand identity preserverΦ.Gait energy images(GEI)is used as the input of network to achieve view transformation of gaits across two various views for cross view gait recognition task.The generator is based on the encoder-decoder structure.First,the input GEI image is disentangled from the view information and the identity information derived of the encoderGenc,which is encoded into the identity feature representationf(x)in the latent space.Next,it is concatenated with the view indicatorv,which is composed of the one-hot coding with the target view assigned 1.To achieve different views of transformation,the concatenated vector as input is melted into the decoderGdecto generate the GEI image from the target view.In order to generate a more accurate gait template in the target view for view transformation task,pixel-wise loss is introduced to constrain the generat

关 键 词：机器视觉 步态识别 跨视角 自注意力 生成对抗网络(GANs) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]