{"title":"Learning gender from human gaits and faces","authors":"Caifeng Shan, S. Gong, P. McOwan","doi":"10.1109/AVSS.2007.4425362","DOIUrl":null,"url":null,"abstract":"Computer vision based gender classification is an important component in visual surveillance systems. In this paper, we investigate gender classification from human gaits in image sequences, a relatively understudied problem. Moreover, we propose to fuse gait and face for improved gender discrimination. We exploit Canonical Correlation Analysis (CCA), a powerful tool that is well suited for relating two sets of measurements, to fuse the two modalities at the feature level. Experiments demonstrate that our multimodal gender recognition system achieves the superior recognition performance of 97.2% in large datasets.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"75 2-3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"50","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AVSS.2007.4425362","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 50
Abstract
Computer vision based gender classification is an important component in visual surveillance systems. In this paper, we investigate gender classification from human gaits in image sequences, a relatively understudied problem. Moreover, we propose to fuse gait and face for improved gender discrimination. We exploit Canonical Correlation Analysis (CCA), a powerful tool that is well suited for relating two sets of measurements, to fuse the two modalities at the feature level. Experiments demonstrate that our multimodal gender recognition system achieves the superior recognition performance of 97.2% in large datasets.