Daisuke Imoto, K. Kurosawa, K. Tsuchiya, K. Kuroki, Manato Hirabayashi, N. Akiba, H. Kakuda
{"title":"服装变化条件下基于特征点和局部形状特征的步态识别评价","authors":"Daisuke Imoto, K. Kurosawa, K. Tsuchiya, K. Kuroki, Manato Hirabayashi, N. Akiba, H. Kakuda","doi":"10.3408/JAFST.745","DOIUrl":null,"url":null,"abstract":" STAGE DOI: 10.3408 / jafst.745 ) Gait recognition is one of recently evolving techniques by which we can recog-nize individuals by one's gait. There are two major approaches; silhouette-based and model-based. In Japan, a method based on GEI ( Gait Energy Image ) , which is one of the silhouette-based approaches, is used for forensic purposes. Sometimes, it is a problem of silhouettes' variabilities in one person due to diŠerent clothing that les-sen recognition reliability under the GEI method. Here, we analyzed and evaluated the average error rates under clothing variation conditions using the method called Dynamic-features method, which we previously proposed. The Dynamic-features method was built inspired by previous studies of model-based gait recognition, which uses time-series of feature points and local shape features around the points automatically extracted from silhouette sequences. Before analysis, we roughly categorize whole data in the OU-ISIR gait database -treadmill dataset B-, which con-tains side-view data, into ˆve clothing categories in order to deal with realistic oŠ-line forensic situation, where we cannot strictly control utilization of dynamic properties of human's gait.","PeriodicalId":14709,"journal":{"name":"Japanese Journal of Forensic Science and Technology","volume":"27 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluation of gait recognition using dynamics of feature points and local shape features under clothing variation conditions\",\"authors\":\"Daisuke Imoto, K. Kurosawa, K. Tsuchiya, K. Kuroki, Manato Hirabayashi, N. Akiba, H. Kakuda\",\"doi\":\"10.3408/JAFST.745\",\"DOIUrl\":null,\"url\":null,\"abstract\":\" STAGE DOI: 10.3408 / jafst.745 ) Gait recognition is one of recently evolving techniques by which we can recog-nize individuals by one's gait. There are two major approaches; silhouette-based and model-based. In Japan, a method based on GEI ( Gait Energy Image ) , which is one of the silhouette-based approaches, is used for forensic purposes. Sometimes, it is a problem of silhouettes' variabilities in one person due to diŠerent clothing that les-sen recognition reliability under the GEI method. Here, we analyzed and evaluated the average error rates under clothing variation conditions using the method called Dynamic-features method, which we previously proposed. The Dynamic-features method was built inspired by previous studies of model-based gait recognition, which uses time-series of feature points and local shape features around the points automatically extracted from silhouette sequences. Before analysis, we roughly categorize whole data in the OU-ISIR gait database -treadmill dataset B-, which con-tains side-view data, into ˆve clothing categories in order to deal with realistic oŠ-line forensic situation, where we cannot strictly control utilization of dynamic properties of human's gait.\",\"PeriodicalId\":14709,\"journal\":{\"name\":\"Japanese Journal of Forensic Science and Technology\",\"volume\":\"27 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Japanese Journal of Forensic Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3408/JAFST.745\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Japanese Journal of Forensic Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3408/JAFST.745","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evaluation of gait recognition using dynamics of feature points and local shape features under clothing variation conditions
STAGE DOI: 10.3408 / jafst.745 ) Gait recognition is one of recently evolving techniques by which we can recog-nize individuals by one's gait. There are two major approaches; silhouette-based and model-based. In Japan, a method based on GEI ( Gait Energy Image ) , which is one of the silhouette-based approaches, is used for forensic purposes. Sometimes, it is a problem of silhouettes' variabilities in one person due to diŠerent clothing that les-sen recognition reliability under the GEI method. Here, we analyzed and evaluated the average error rates under clothing variation conditions using the method called Dynamic-features method, which we previously proposed. The Dynamic-features method was built inspired by previous studies of model-based gait recognition, which uses time-series of feature points and local shape features around the points automatically extracted from silhouette sequences. Before analysis, we roughly categorize whole data in the OU-ISIR gait database -treadmill dataset B-, which con-tains side-view data, into ˆve clothing categories in order to deal with realistic oŠ-line forensic situation, where we cannot strictly control utilization of dynamic properties of human's gait.