{"title":"基于位置和纹理信息联合分布的统计人脸特征提取","authors":"M. Yilmaz, Hakan Erdogan, M. Unel","doi":"10.1109/SIU.2009.5136471","DOIUrl":null,"url":null,"abstract":"A facial feature extraction method is proposed in this work, which uses location and texture information given a face image. Location and texture information can automatically be learnt by the system, from a training data. Best facial feature locations are found by maximizing the joint distribution of location and texture information of facial features. Performance of the method was found promising after it is tested using 100 test images. Also it is observed that this new method performs better than active appearance models for the same test data.","PeriodicalId":219938,"journal":{"name":"2009 IEEE 17th Signal Processing and Communications Applications Conference","volume":"196 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Statistical facial feature extraction using joint distribution of location and texture information\",\"authors\":\"M. Yilmaz, Hakan Erdogan, M. Unel\",\"doi\":\"10.1109/SIU.2009.5136471\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A facial feature extraction method is proposed in this work, which uses location and texture information given a face image. Location and texture information can automatically be learnt by the system, from a training data. Best facial feature locations are found by maximizing the joint distribution of location and texture information of facial features. Performance of the method was found promising after it is tested using 100 test images. Also it is observed that this new method performs better than active appearance models for the same test data.\",\"PeriodicalId\":219938,\"journal\":{\"name\":\"2009 IEEE 17th Signal Processing and Communications Applications Conference\",\"volume\":\"196 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE 17th Signal Processing and Communications Applications Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2009.5136471\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE 17th Signal Processing and Communications Applications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2009.5136471","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Statistical facial feature extraction using joint distribution of location and texture information
A facial feature extraction method is proposed in this work, which uses location and texture information given a face image. Location and texture information can automatically be learnt by the system, from a training data. Best facial feature locations are found by maximizing the joint distribution of location and texture information of facial features. Performance of the method was found promising after it is tested using 100 test images. Also it is observed that this new method performs better than active appearance models for the same test data.