淡江大學機構典藏:Item 987654321/75807
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    题名: Fast Gender Recognition by Using a Shared-Integral-Image Approach
    作者: Shen, Bau-cheng;Chen, Chu-song;Hsu, Hui-huang
    贡献者: 淡江大學資訊工程學系
    关键词: AdaBoost;Gender Recognition;Integral Image;Real AdaBoost;Support Vector Machine
    日期: 2009-04
    上传时间: 2012-04-16 09:54:46 (UTC+8)
    出版者: IEEE Computer Society
    摘要: We develop a new approach for gender recognition. In this paper, 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 a gender identifier. We then use nonlinear support vector machines for classification, and obtain more accurate identification results.
    關聯: Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2009), pp.521-524
    DOI: 10.1109/ICASSP.2009.4959635
    显示于类别:[資訊工程學系暨研究所] 會議論文

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    FAST GENDER RECOGNITION BY USING A SHARED-INTEGRAL-IMAGE APPROACH.pdf全文檔462KbAdobe PDF325检视/开启
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