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研究生： 陳映濃
Ying-Nong Chen
論文名稱： 應用最短特徵空間轉換法於人臉辨識
Face Recognition Using Nearest Feature Space Embedding
指導教授： 范國清
Kuo-Chin Fan
韓欽銓
Chin-Chuan Han
口試委員:
學位類別： 博士
Doctor
系所名稱： 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
畢業學年度： 99
語文別： 英文
論文頁數： 63
中文關鍵詞： 人臉辨識 、區域資訊
外文關鍵詞： laplacian, face recognition, manifold
相關次數： 點閱：9  下載：0 
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    • 人臉辨識演算法通常必須克服一些問題，如角度、光線以及表情變化。為了降低這些問題的影響，已經有許多學者不斷地研究如何在特徵空間中尋找最佳的線性或非線性區別轉換方式以達到較佳的辨識結果。在本研究中，我們提出了一種基於最近特徵空間轉換法的人臉辨識演算法。這種方法是將一個資料點與其最鄰近的資料所構成的直線或是空間，將這一段距離嵌入到區別分析中。透過這樣的方式，可以將類別資訊、區域結構拓樸保留以及最近特徵空間測量這三個因子嵌入到最佳化的區別分析中。在實驗結果中，我們的演算法與其它著名演算法在比較後都有較佳的辨識結果。

    Face recognition algorithms often have to solve problems such as facial pose, illumination, and expression (PIE). To reduce these impacts, many researchers have been trying to find the best discriminant transformation in feature spaces, either linear or nonlinear, to obtain better recognition. Various researchers have also designed novel matching algorithms to reduce the PIE effects. In this study, a nearest feature space embedding (called NFS embedding) algorithm is proposed for face recognition. The distance between a point and the nearest feature line (NFL) or an NFS is embedded in the transformation in the discriminant analysis. Three factors, namely class separability, neighborhood structure preservation, and NFS measurement, were considered to find the most effective and discriminating transformation in eigenspaces. The proposed method was evaluated using several benchmark databases and compared with several state-of-the-art algorithms. According to the compared results, the proposed method outperformed the other algorithms.

    摘要 V Abstract VII 誌謝 VII Chapter 1：Introduction 1 1.1 Motivation 1 1.2 Organization of the Dissertation 1 Chapter 2：Review of Related Works 3 2.1 Previous Methods for Face Recognition 3 2.2 A Review of Eigenspace Approach 9 2.2.1 Linear Discriminant Analysis(LDA)………….…………………...11 2.2.2 Local Structure Preserving Algorithm…..…….…………………...12 2.2.3 Optimization of the Fisher Criterion…..…….………………..…...14 2.2.4 Discriminative Common Vectors(DCV)…..…...……………..…...16 2.2.5 Two-dimensional Principal Component Analysis……...……..…...16 Chapter 3：Face Recognition Using Nearest Feature Space Embedding 18 3.1 Nearest Feature Space Embedding(NFS) 18 3.1.1 Nearest Feature Space (NFS) Strategy 21 3.1.2 Scatter Computation of the Nearest Feature Space 22 3.2.3 Maximization of the Fisher criterion 28 3.2 Two-dimensional NFS Embedding(2DNFS) 31 Chapter 4：Experimental Results 34 4.1 Evaluation of Nearest Feature Space Embedding 34 4.2 Evaluation of 2DNFS Embedding 55 Chapter 5：Conclusions and Future Works 57 5.1 Conclusions 57 5.2 Future Works 57 References 58

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