Runsheng Wang;Yuxuan Shi;Hefei Ling;Zongyi Li;Ping Li;Boyuan Liu;Hanqing Zheng;Qian Wang
{"title":"Gait Recognition via Gait Period Set","authors":"Runsheng Wang;Yuxuan Shi;Hefei Ling;Zongyi Li;Ping Li;Boyuan Liu;Hanqing Zheng;Qian Wang","doi":"10.1109/TBIOM.2023.3244206","DOIUrl":null,"url":null,"abstract":"Gait recognition has promising application prospects in surveillance applications, with the recently proposed video-based gait recognition methods affording huge progress. However, due to the poor image quality of some gait frames, the original frame-level features extracted from gait silhouettes are not discriminative enough to be aggregated as gait features utilized during the final recognition. Besides, as a type of periodic biometric behavior, periodic gait information is considered efficacious for capturing typical human walking patterns and refining frame-level gait features. Therefore, this paper proposes a novel approach that exploits periodic gait information, named Gait Period Set (GPS), which divides the gait period into several phases and ensembles the gait phase features to refine frame-level features. Then, features from different phases are aggregated into a video-level feature. Moreover, the refined frame-level features are aggregated as the refined gait phase features with higher quality, which can be used to re-refine the frame-level features. Hence, we upgrade the GPS into the Iterative Gait Period Set (IGPS) to iteratively refine the frame-level features. The results of extensive experiments on prevailing gait recognition datasets validate the effectiveness of the GPS and IGPS modules and demonstrate that the proposed method achieves state-of-the-art performance.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"5 2","pages":"183-195"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on biometrics, behavior, and identity science","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10042966/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Gait recognition has promising application prospects in surveillance applications, with the recently proposed video-based gait recognition methods affording huge progress. However, due to the poor image quality of some gait frames, the original frame-level features extracted from gait silhouettes are not discriminative enough to be aggregated as gait features utilized during the final recognition. Besides, as a type of periodic biometric behavior, periodic gait information is considered efficacious for capturing typical human walking patterns and refining frame-level gait features. Therefore, this paper proposes a novel approach that exploits periodic gait information, named Gait Period Set (GPS), which divides the gait period into several phases and ensembles the gait phase features to refine frame-level features. Then, features from different phases are aggregated into a video-level feature. Moreover, the refined frame-level features are aggregated as the refined gait phase features with higher quality, which can be used to re-refine the frame-level features. Hence, we upgrade the GPS into the Iterative Gait Period Set (IGPS) to iteratively refine the frame-level features. The results of extensive experiments on prevailing gait recognition datasets validate the effectiveness of the GPS and IGPS modules and demonstrate that the proposed method achieves state-of-the-art performance.