A Triplet-Branch Convolutional Neural Network for Part-Based Gait Recognition

作     者：Sang-Soo Yeo Seungmin Rho Hyungjoon Kim Jibran Safdar Umar Zia Mehr Yahya Durrani 

作者机构：Department of Computer EngineeringMokwon UniversityDaejeonKorea Department of Industrial SecurityChung-Ang UniversitySeoul06974Korea Department of Computer EngineeringSemyung UniversityJechun-siKorea Department of Computer ScienceUET TaxilaTaxilaPakistan Department of Computer ScienceCOMSATS University IslamabadAttock CampusAttockPakistan 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第47卷第11期

页      面：2027-2047页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2022R1F1A1063134) the MSIT (Ministry of Science and ICT),Korea,under the ITRC (Information Technology Research Center)Support Program (IITP-2022-2018-0-01799)supervised by the IITP (Institute for Information&communications Technology Planning&Evaluation) 

主　　题：Vision-based surveillance systems deep learning triplet-branch CNN gait recognition covariate conditions 

摘      要：Intelligent vision-based surveillance systems are designed to deal with the gigantic volume of videos captured in a particular environment to perform the interpretation of scenes in form of detection,tracking,monitoring,behavioral analysis,and *** addition to that,another evolving way of surveillance systems in a particular environment is human gait-based *** the existing research,several methodological frameworks are designed to use deep learning and traditional methods,nevertheless,the accuracies of these methods drop substantially when they are subjected to covariate *** covariate variables disrupt the gait features and hence the recognition of subjects becomes *** handle these issues,a region-based triplet-branch Convolutional Neural Network(CNN)is proposed in this research that is focused on different parts of the human Gait Energy Image(GEI)including the head,legs,and body separately to classify the subjects,and later on,the final identification of subjects is decided by probability-based majority voting ***,to enhance the feature extraction and draw the discriminative features,we have added soft attention layers on each branch to generate the soft attention *** proposed model is validated on the CASIA-B database and findings indicate that part-based learning through triplet-branch CNN shows good performance of 72.98%under covariate conditions as well as also outperforms single-branch CNN models.