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Face recognition rate comparison using Principal Component Analysis in Wavelet compression image  

박장한 (광운대학교 컴퓨터공학과)
남궁재찬 (광운대학교 컴퓨터공학과)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.41, no.5, 2004 , pp. 33-40 More about this Journal
Abstract
In this paper, we constructs face database by using wavelet comparison, and compare face recognition rate by using principle component analysis (Principal Component Analysis : PCA) algorithm. General face recognition method constructs database, and do face recognition by using normalized size. Proposed method changes image of normalized size (92${\times}$112) to 1 step, 2 step, 3 steps to wavelet compression and construct database. Input image did compression by wavelet and a face recognition experiment by PCA algorithm. As well as method that is proposed through an experiment reduces existing face image's information, the processing speed improved. Also, original image of proposed method showed recognition rate about 99.05%, 1 step 99.05%, 2 step 98.93%, 3 steps 98.54%, and showed that is possible to do face recognition constructing face database of large quantity.
Keywords
Wavelet Compression; Face Recognition; PCA; Eigenvalue; Eigenvector;
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