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研究生: 詹依佳
論文名稱: 利用機率圖模型於影片上之人臉辨識研究
Video-Based Face Recognition Using A Probabilistic Graphical Model
指導教授: 李忠謀
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2009
畢業學年度: 97
語文別: 中文
論文頁數: 48
中文關鍵詞: 人臉辨識機率圖模型二維線性鑑別分析法高斯分佈
英文關鍵詞: face recognition, probabilistic graphical model, two-dimensional linear discriminant analysis(2DLDA), Gaussian distribution
論文種類: 學術論文
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  • 針對影片上的人臉辨識問題,本論文提出一個機率圖模型來解決並將其公式化。首先,我們將此問題分成兩個部份來探討,分別為相似度之計算與遞移機率,其中相似度之計算可被視作為傳統的單張影像之人臉辨識的結果,在此篇論文中,我們採用二維線性鑑別分析法(2DLDA)摘取特徵,再藉由高斯分佈來估算相似度。而遞移機率則是計算先前時間點的狀態轉移到此時間點的狀態之機率,我們可將遞移機率分成兩個部份來估算,其一為人與人的遞移機率,另一個則為姿勢轉換的遞移機率,希望藉由相鄰影像的時間關係修正錯誤的辨識結果與提升準確率。在本論文的實驗中,我們使用在國際上常採用的 Honda/UCSD 資料庫以及本實驗室自行建立的VIPlab資料庫。實驗證明本研究提出之方法可適用於不同的資料庫,且實驗結果也有90%以上的正確率。

    We present a probabilistic graphical model to formulate and deal with video-based face recognition. Our formulation divides the problem into two parts: one for likelihood measure and the other for transition measure. The likelihood measure can be regarded as a traditional task of face recognition within a single image, i.e., to estimate how similar to a specified person this observing face image is. In our work, two-dimensional linear discriminant analysis (2DLDA) is employed for feature extraction, and then we use a Gaussian distribution to assess the likelihood measure. The transition measure is estimated via two terms, person transition and pose transition. The transition terms could fix some incorrect recognition results because of considering the information between adjacent frames. In the face recognition experiments, we adopt two datasets, Honda/UCSD dataset and VIPlab dataset. Finally, it is demonstrated that our proposed approach is robust in different datasets and produces good recognition accuracy which is more than 90%.

    目錄 I 附圖目錄 III 附表目錄 V 第一章 緒論 1 1-1 研究動機 1 1-2 研究目的 2 1-3 研究範圍與限制 2 1-4 論文架構 3 第二章 文獻探討 4 2-1 單張影像上之人臉辨識 4 2-2 影片上之人臉辨識 8 2-2.1 未加入相鄰影像之資訊 9 2-2.2 加入相鄰影像之資訊 10 第三章 理論基礎 14 3-1 狀態空間模型(State Space Model) 14 3-2 二維線性鑑別分析法(2DLDA) 15 第四章 機率圖模型之人臉辨識 20 4-1 機率圖模型與公式推導 20 4-2 相似度之計算 23 4-3 遞移機率 24 4-3.1 人與人的遞移機率 25 4-3.2 姿勢轉換的遞移機率 26 4-4 流程與方法總結 27 第五章 實驗結果與分析 31 5-1 Honda/UCSD 資料庫 31 5-2 本研究使用Honda/UCSD 資料庫之實驗 32 5-2.1 訓練流程 33 5-2.2 辨識結果分析 36 5-2.3 辨識率估算 38 5-2.4 機率分佈圖之探討 39 5-3 單張影像上人臉辨識技術之比較 40 5-4 使用VIPlab 資料庫之辨識結果 41 第六章 結論與未來展望 44 參考文獻 45

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