{"title":"基于LDA和LPCC的高性能虹膜识别","authors":"Chia-te Chu, Ching-Han Chen","doi":"10.1109/ICTAI.2005.71","DOIUrl":null,"url":null,"abstract":"In this paper, the iris recognition algorithm based on LPCC and LDA is first presented. So far, the two algorithms are not found for iris recognition in literature. In addition, a simple and fast training algorithm, particle swarm optimization (PSO), is also introduced for training the probabilistic neural network (PNN). Finally, a comparative experiment of existing methods for iris recognition is evaluated on CASIA iris image databases. The proposed algorithms can achieve 100% recognition rates and the result is encouraging","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"51","resultStr":"{\"title\":\"High performance iris recognition based on LDA and LPCC\",\"authors\":\"Chia-te Chu, Ching-Han Chen\",\"doi\":\"10.1109/ICTAI.2005.71\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the iris recognition algorithm based on LPCC and LDA is first presented. So far, the two algorithms are not found for iris recognition in literature. In addition, a simple and fast training algorithm, particle swarm optimization (PSO), is also introduced for training the probabilistic neural network (PNN). Finally, a comparative experiment of existing methods for iris recognition is evaluated on CASIA iris image databases. The proposed algorithms can achieve 100% recognition rates and the result is encouraging\",\"PeriodicalId\":294694,\"journal\":{\"name\":\"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"51\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTAI.2005.71\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI.2005.71","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
High performance iris recognition based on LDA and LPCC
In this paper, the iris recognition algorithm based on LPCC and LDA is first presented. So far, the two algorithms are not found for iris recognition in literature. In addition, a simple and fast training algorithm, particle swarm optimization (PSO), is also introduced for training the probabilistic neural network (PNN). Finally, a comparative experiment of existing methods for iris recognition is evaluated on CASIA iris image databases. The proposed algorithms can achieve 100% recognition rates and the result is encouraging
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