{"title":"人脸识别系统采用HMM-PSO进行特征选择","authors":"Mai Mohamed Mahmoud Farag, T. Elghazaly, H. Hefny","doi":"10.1109/ICENCO.2016.7856453","DOIUrl":null,"url":null,"abstract":"In this paper we apply particle swarm optimization (PSO) feature selection to enhance Hidden Markov Model (HMM) states and parameters for face recognition systems. Ideal Feature selection for face images based on the idea of collaborative behavior of bird flocking to reduce the feature size and hence recognition time complicity. The framework has been inspected on 400 face pictures of the Olivetti Research Laboratory face database. The experiments demonstrated an acknowledgment rate of 98.5%, using half of the images for training.","PeriodicalId":332360,"journal":{"name":"2016 12th International Computer Engineering Conference (ICENCO)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Face recognition system using HMM-PSO for feature selection\",\"authors\":\"Mai Mohamed Mahmoud Farag, T. Elghazaly, H. Hefny\",\"doi\":\"10.1109/ICENCO.2016.7856453\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we apply particle swarm optimization (PSO) feature selection to enhance Hidden Markov Model (HMM) states and parameters for face recognition systems. Ideal Feature selection for face images based on the idea of collaborative behavior of bird flocking to reduce the feature size and hence recognition time complicity. The framework has been inspected on 400 face pictures of the Olivetti Research Laboratory face database. The experiments demonstrated an acknowledgment rate of 98.5%, using half of the images for training.\",\"PeriodicalId\":332360,\"journal\":{\"name\":\"2016 12th International Computer Engineering Conference (ICENCO)\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 12th International Computer Engineering Conference (ICENCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICENCO.2016.7856453\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 12th International Computer Engineering Conference (ICENCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICENCO.2016.7856453","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Face recognition system using HMM-PSO for feature selection
In this paper we apply particle swarm optimization (PSO) feature selection to enhance Hidden Markov Model (HMM) states and parameters for face recognition systems. Ideal Feature selection for face images based on the idea of collaborative behavior of bird flocking to reduce the feature size and hence recognition time complicity. The framework has been inspected on 400 face pictures of the Olivetti Research Laboratory face database. The experiments demonstrated an acknowledgment rate of 98.5%, using half of the images for training.
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