{"title":"基于相关滤波和欧氏距离的手型识别","authors":"W. Zailah, M. I. Solihin, W. Lee.S., Ang. C.K.","doi":"10.20967/JCSCM.2019.01.003","DOIUrl":null,"url":null,"abstract":"This paper proposes a handshape recognition based on correlation filter and Euclidean distance. Unlike biometric and face verification system, handshape is rarely used for verification of an individual. Therefore, handshape will be used as an alternative way for human identification and authentication for this system. The performance for the minimum average correlation energy (MACE) filter and Euclidean distance are evaluated using a new database.","PeriodicalId":374608,"journal":{"name":"Journal of Computer Science & Computational Mathematics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Handshape Recognition Using Correlation Filter and Euclidean Distance\",\"authors\":\"W. Zailah, M. I. Solihin, W. Lee.S., Ang. C.K.\",\"doi\":\"10.20967/JCSCM.2019.01.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a handshape recognition based on correlation filter and Euclidean distance. Unlike biometric and face verification system, handshape is rarely used for verification of an individual. Therefore, handshape will be used as an alternative way for human identification and authentication for this system. The performance for the minimum average correlation energy (MACE) filter and Euclidean distance are evaluated using a new database.\",\"PeriodicalId\":374608,\"journal\":{\"name\":\"Journal of Computer Science & Computational Mathematics\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computer Science & Computational Mathematics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.20967/JCSCM.2019.01.003\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Science & Computational Mathematics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20967/JCSCM.2019.01.003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Handshape Recognition Using Correlation Filter and Euclidean Distance
This paper proposes a handshape recognition based on correlation filter and Euclidean distance. Unlike biometric and face verification system, handshape is rarely used for verification of an individual. Therefore, handshape will be used as an alternative way for human identification and authentication for this system. The performance for the minimum average correlation energy (MACE) filter and Euclidean distance are evaluated using a new database.