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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/34962


    Title: 以非線性內插映射法估算頭部角度
    Other Titles: Head pose estimation based on nonlinear interpolative mapping
    Authors: 張振維;Chang, Chen-wei
    Contributors: 淡江大學資訊工程學系碩士班
    林慧珍;Lin, Hwei-jen
    Keywords: 人臉辨識;頭部角度辨識;輻狀基底函數;流型;非線性內插映射;face recognition;Head pose estimation;Isomap;Radial Basis Function (RBF);Nonlinear interpolative mapping
    Date: 2009
    Issue Date: 2010-01-11 05:49:44 (UTC+8)
    Abstract: 大部分的人臉辨識系統只對於接近正面的臉部影像有較好的辨識結果,對於方向角度變化較多的頭部影像,往往無法成功辨識。然而若是能夠先對人臉影像計算其頭部方向角度或是根據角度做方位分類,再進入分類後的人臉影像資料庫進行搜尋比對,必能大大提高辨識率。
    本篇論文提出了ㄧ個人臉影像之頭部角度辨識(估計)與方向分類方法。本方法利是應用輻狀基底函數(Radial Basis Function,RBF),監督式訓練一個從輸入影像至特徵向量之非線性內插映射(Nonlinear interpolative mapping),對角度介於0度到360度的人臉影像辨識其角度;本方法與N. Hu et al. [1]所提的非監督式訓練非線性嵌入與映射方法比較,實驗結果顯示本篇論文提出方法無論在精確性或時間效率方面都有較好的結果。
    The performance of face recognition systems depends on conditions being consistent, including lighting, pose and facial expression. To solve the problem produced by pose variation it is suggested to pre-estimate the pose orientation of the given head image before it is recognized. In this paper, we propose a head pose estimation method that is an improvement on the one proposed by N. Hu et al. [1]. The proposed method trains in a supervised manner a nonlinear interpolative mapping function that maps input images to predicted pose angles. This mapping function is a linear combination of some Radial Basis Functions (RBF). The experimental results show that our proposed method has a better performance than the method proposed by Nan Hu et al. in terms of both time efficiency and estimation accuracy.
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Thesis

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