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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/59920

    题名: Gender Recognition from Human Faces by Using a Shared-Integral-Image Approach
    作者: Shen, Bao-cheng;Hsu, Hui-huang;Chen, Chu-song
    贡献者: 淡江大學資訊工程學系
    关键词: Gender Recognition;AdaBoost;Real Ad-aBoost;Support Vector Machine;Integral Image
    日期: 2009-05
    上传时间: 2011-10-05 22:27:03 (UTC+8)
    出版者: Allahabad: Pushpa Publishing House
    摘要: We develop an approach for gender recognition based on human faces. We combine rectangle features extracted from the human-face region into a rectangle-feature vector (RFV). The RFV is computationally fast and effective in encoding intensity variations of local regions of human face. By only using few rectangle features learned by AdaBoost, we present an effective gender identification approach. We then use nonlinear support vector machines for classification, and obtain more accurate recognition results. Experimental results show that our approach performs well for the Feret database.
    關聯: Far East Journal of Experimental and Theoretical Artificial Intelligence 3(2), pp.101-112
    显示于类别:[資訊工程學系暨研究所] 期刊論文





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