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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/74573

    Title: 在雲端計算中使用支援向量機來做性別分類
    Other Titles: Using support vector machines for gender classification on cloud computing
    Authors: 周俊昌;Chou, Chun-Chang
    Contributors: 淡江大學資訊工程學系碩士在職專班
    洪文斌;Horng, Wen-Bing
    Keywords: 支援向量機;區域二元模式;性別辨識;雲端計算;Cloud Computing;Gender classification;LBP;SVM
    Date: 2011
    Issue Date: 2011-12-28 18:55:34 (UTC+8)
    Abstract: 在近幾年電腦視覺影像研究中,人臉的辨識上已有相當大的進步,延伸而來的應用也相當多,如人臉的性別、表情、年齡辨識等。在這些影像的辨識研究領域中,系統的辨識率以及系統的效能,一直是相關領域的研究重點。
    Face detection has been considerable progress in computer vision recently, and extends from the applications are quite a few, such as gender recognition, expression, age identification. In the field of computer vision, recognition rate and system performance has been attention in the related field.
    In this paper, client device is responsible for detecting the location of face then reduce dimension of the face feature using LBP (local binary patterns) operator, and send to server side. The classification problem is performed by support vector machines.
    The support vector machine algorithm requests amount of computing and memory, which is a considerable burden on handheld computers or mobile phones. To solve the problem, we developed client-server programming. In order to avoid that client device over loading, the server side is responsible for training of gender classifier and predicts results which received the face feature form client device then sends to the client device. In our experiments, 5-fold cross-validation are performed on FERET face database and obtained the accuracy of 93.21%.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

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