{"title":"基于主成分分析算法的三维人脸识别方法","authors":"Xue Yuan, Jianming Lu, T. Yahagi","doi":"10.1109/ISCAS.2005.1465311","DOIUrl":null,"url":null,"abstract":"We present a method of face recognition using 3D images. We first compensate the poses of 3D original facial images using geometrical measurement and extract 2D texture data and the 3D shape data from 3D facial images for recognition. Based on a principal component analysis (PCA) algorithm, all the 2D texture images and the 3D shape images are normalized to 32/spl times/32 pixels. In the second step, we propose a method for face recognition based on fuzzy clustering and parallel neural networks. Experimental results for 70 persons with different poses demonstrate the efficiency of our algorithm.","PeriodicalId":191200,"journal":{"name":"2005 IEEE International Symposium on Circuits and Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"A method of 3D face recognition based on principal component analysis algorithm\",\"authors\":\"Xue Yuan, Jianming Lu, T. Yahagi\",\"doi\":\"10.1109/ISCAS.2005.1465311\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a method of face recognition using 3D images. We first compensate the poses of 3D original facial images using geometrical measurement and extract 2D texture data and the 3D shape data from 3D facial images for recognition. Based on a principal component analysis (PCA) algorithm, all the 2D texture images and the 3D shape images are normalized to 32/spl times/32 pixels. In the second step, we propose a method for face recognition based on fuzzy clustering and parallel neural networks. Experimental results for 70 persons with different poses demonstrate the efficiency of our algorithm.\",\"PeriodicalId\":191200,\"journal\":{\"name\":\"2005 IEEE International Symposium on Circuits and Systems\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2005 IEEE International Symposium on Circuits and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCAS.2005.1465311\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 IEEE International Symposium on Circuits and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCAS.2005.1465311","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A method of 3D face recognition based on principal component analysis algorithm
We present a method of face recognition using 3D images. We first compensate the poses of 3D original facial images using geometrical measurement and extract 2D texture data and the 3D shape data from 3D facial images for recognition. Based on a principal component analysis (PCA) algorithm, all the 2D texture images and the 3D shape images are normalized to 32/spl times/32 pixels. In the second step, we propose a method for face recognition based on fuzzy clustering and parallel neural networks. Experimental results for 70 persons with different poses demonstrate the efficiency of our algorithm.
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