Chengyun Liu, Zhenxue Chen, F. Chang, Kaifang Wang
{"title":"基于改进人脸模型的人脸识别算法","authors":"Chengyun Liu, Zhenxue Chen, F. Chang, Kaifang Wang","doi":"10.1109/ICNC.2014.6975963","DOIUrl":null,"url":null,"abstract":"Face recognition is one of typical biometric identification method, which has a great prospect in secure authentication system, file management, human-computer interaction and social security. This paper proposes gray-scale characteristics and creates facial templates to recognize faces method based on a given number of samples. Firstly, it selects the method of building template according to the number of samples to create the facial template image; then, it will compare the difference of first-order edge entropy between recognition image and the template image and find the best match result; finally, the recognition result is output. Experimental results show that the proposed algorithm has good recognition effect on face recognition under non-constraint conditions.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Face recognition algorithm based on improved facial model\",\"authors\":\"Chengyun Liu, Zhenxue Chen, F. Chang, Kaifang Wang\",\"doi\":\"10.1109/ICNC.2014.6975963\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Face recognition is one of typical biometric identification method, which has a great prospect in secure authentication system, file management, human-computer interaction and social security. This paper proposes gray-scale characteristics and creates facial templates to recognize faces method based on a given number of samples. Firstly, it selects the method of building template according to the number of samples to create the facial template image; then, it will compare the difference of first-order edge entropy between recognition image and the template image and find the best match result; finally, the recognition result is output. Experimental results show that the proposed algorithm has good recognition effect on face recognition under non-constraint conditions.\",\"PeriodicalId\":208779,\"journal\":{\"name\":\"2014 10th International Conference on Natural Computation (ICNC)\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 10th International Conference on Natural Computation (ICNC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2014.6975963\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 10th International Conference on Natural Computation (ICNC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2014.6975963","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Face recognition algorithm based on improved facial model
Face recognition is one of typical biometric identification method, which has a great prospect in secure authentication system, file management, human-computer interaction and social security. This paper proposes gray-scale characteristics and creates facial templates to recognize faces method based on a given number of samples. Firstly, it selects the method of building template according to the number of samples to create the facial template image; then, it will compare the difference of first-order edge entropy between recognition image and the template image and find the best match result; finally, the recognition result is output. Experimental results show that the proposed algorithm has good recognition effect on face recognition under non-constraint conditions.