{"title":"多重选择:学生考勤系统中人脸识别的实践研究","authors":"Zhiyao Zhang, Benjamin Ma Di","doi":"10.1145/3313950.3313954","DOIUrl":null,"url":null,"abstract":"This paper presents a new approach, Multiplet Selection, for multi-faces recognition and its application in student attendance system. Instead of using a linear classifier such as SVM to classify face feature vectors, we adopt a \"multiplet selection\" approach such that Euclidean distances score between each identity's Anchor face [4] and a random input face are computed. Together with a pre-determined threshold parameter, this score is used for input face-identity pair association. We also develop a student attendance system based on the proposed multi-face recognition algorithm. And testing results video are available at the following URL: https://youtu.be/OZOgcw7B1YI.","PeriodicalId":392037,"journal":{"name":"Proceedings of the 2nd International Conference on Image and Graphics Processing","volume":"164 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Multiplet selection: a practical study of multi-faces recognition for student attendance system\",\"authors\":\"Zhiyao Zhang, Benjamin Ma Di\",\"doi\":\"10.1145/3313950.3313954\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a new approach, Multiplet Selection, for multi-faces recognition and its application in student attendance system. Instead of using a linear classifier such as SVM to classify face feature vectors, we adopt a \\\"multiplet selection\\\" approach such that Euclidean distances score between each identity's Anchor face [4] and a random input face are computed. Together with a pre-determined threshold parameter, this score is used for input face-identity pair association. We also develop a student attendance system based on the proposed multi-face recognition algorithm. And testing results video are available at the following URL: https://youtu.be/OZOgcw7B1YI.\",\"PeriodicalId\":392037,\"journal\":{\"name\":\"Proceedings of the 2nd International Conference on Image and Graphics Processing\",\"volume\":\"164 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-02-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2nd International Conference on Image and Graphics Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3313950.3313954\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2nd International Conference on Image and Graphics Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3313950.3313954","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multiplet selection: a practical study of multi-faces recognition for student attendance system
This paper presents a new approach, Multiplet Selection, for multi-faces recognition and its application in student attendance system. Instead of using a linear classifier such as SVM to classify face feature vectors, we adopt a "multiplet selection" approach such that Euclidean distances score between each identity's Anchor face [4] and a random input face are computed. Together with a pre-determined threshold parameter, this score is used for input face-identity pair association. We also develop a student attendance system based on the proposed multi-face recognition algorithm. And testing results video are available at the following URL: https://youtu.be/OZOgcw7B1YI.