{"title":"Optimum selection of features for 2D (color) and 3D (depth) face recognition using modified PCA (2D)","authors":"G. Vijayalakshmi, A. Raj, S. V. S. K. Ashok Varma","doi":"10.1109/ICSSS.2014.7006175","DOIUrl":null,"url":null,"abstract":"The paper proposes a Modified Principal Component Analysis coined as 2DPCA to compare 2D and 3D face recognition. In 2DPCA a covariance matrix of image is obtained directly from the original image and is used to find the eigenvectors for image feature extraction. Here the Texas 3D [1] face recognition database was considered, which has 1149 pairs of high resolution, preprocessed and pose normalized color and range images. These images are pixel-to-pixel registered and of resolution of 751×501 pixels. The experiment performed using the images reconstructed from feature vectors demonstrated that depth information was beneficial in representing and recognizing the face with least number of principal components.","PeriodicalId":354879,"journal":{"name":"2014 International Conference on Smart Structures and Systems (ICSSS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Smart Structures and Systems (ICSSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSSS.2014.7006175","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The paper proposes a Modified Principal Component Analysis coined as 2DPCA to compare 2D and 3D face recognition. In 2DPCA a covariance matrix of image is obtained directly from the original image and is used to find the eigenvectors for image feature extraction. Here the Texas 3D [1] face recognition database was considered, which has 1149 pairs of high resolution, preprocessed and pose normalized color and range images. These images are pixel-to-pixel registered and of resolution of 751×501 pixels. The experiment performed using the images reconstructed from feature vectors demonstrated that depth information was beneficial in representing and recognizing the face with least number of principal components.
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