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    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/987654321/98197

    題名: A Shared-Integral-Image Approach for Fast Gender Recognition
    作者: Shen, Bau-Cheng;Chen, Chu-Song;Hsu, Hui-Huang
    貢獻者: 淡江大學資訊工程學系
    關鍵詞: Gender Recognition;AdaBoost, Real AdaBoost;Support Vector Machine;Integral Image
    日期: 2008-11
    上傳時間: 2014-06-13 15:44:23 (UTC+8)
    出版者: 臺北縣淡水鎮 : 淡江大學
    摘要: In this paper, we develop a new approach for gender recognition. Our approach uses the rectangle feature vector (RFV) as a representation to identify humans' gender from their faces. The RFV is computationally fast and effective to encode intensity variations of local . regions of human face. By only using few rectangle features learned by AdaBoost, we present an effective gender identifier. We then use nonlinear support vector machines for classification, and obtain more accurate identification results. Experimental results show that our approach performs well for the Feret database.
    關聯: 第十三屆人工智慧與應用研討會論文集=The 13th conference on artificial intelligence and applications, pp.13-17
    顯示於類別:[資訊工程學系暨研究所] 會議論文


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