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研究生:龔信文
研究生(外文):Kung, Hsin-Wen
論文名稱:雙子空間非負圖形嵌入表示法及其於人臉表情辨識的應用
論文名稱(外文):Dual Subspace Nonnegative Graph Embedding for Person-Invariant Facial Expression Recognition
指導教授:許秋婷許秋婷引用關係
指導教授(外文):Hsu, Chiou-Ting
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:44
中文關鍵詞:非負矩陣分解臉部表情辨識圖形嵌入表示法雙子空間
外文關鍵詞:Nonnegative Matrix FactorizationFacial Expression RecognitionGraph-EmbeddingDual Subspace
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不同身份的人具有不同的面貌,對於相同表情的詮釋方式也隨之不同。此一受身份影響的特性,大大增加自動化表情辨識的困難度。在本文之中,我們提出雙子空間的非負圖形嵌入表示法,將臉部表情影像使用身份以及表情子空間加以描述。其中,身份子空間描述和身份有關的臉部變異;而表情子空間則描述不受身份影響的表情變異。透過本方法,我們可將輸入的臉部影像,透過對應的非負基底,分解為身份部份和表情部份。除此之外,對於相同表情的類別內變異,我們重新設計表情子空間的圖形限制以解決此一問題。我們透過CK+、JAFFE和TFEID三種人臉表情資料庫進行驗證;實驗結果顯示,我們的方法均優於現有的表情辨識方法。
Different persons usually exhibit various appearance changes when posing the same expression. This person-dependent behavior often complicates automatic facial expression recognition. In this thesis, to address the person-independent expression recognition problem, we propose a Dual Subspace Nonnegative Graph Embedding (DSNGE) to represent expressive images using two subspaces: identity and expression subspaces. The identity subspace characterizes person-dependent appearance variations; whereas the expression subspace characterizes person-independent expression variations. With DSNGE, we decompose each facial image into an identity part and an expression part represented by their corresponding nonnegative bases. We also address the intra-class variations issue in the expression recognition problem, and further devise a graph-embedding constraint on the expression subspace to tackle this problem. Our experimental results show that the proposed DSNGE outperforms other graph-based nonnegative factorization methods and existing expression recognition methods on CK+, JAFFE and TFEID databases.
中文摘要 1
Abstract 2

1. Introduction 4

2. Related Work 9
2.1 Nonnegative Matrix Factorization (NMF) 9
2.2 Graph-based Nonnegative Matrix Factorization 10
2.3 Robust Nonnegative Graph Embedding 13

3. Overview: Dual Subspace Nonnegative Matrix Factorization 14

4. Proposed Method 17
4.1 Remarks on Dual Subspace Nonnegative Matrix Factorization 17
4.2 Dual Subspace Nonnegative Graph Embedding 18
4.3 Multiplicative Iterative Solution 20
4.3.1 Optimize W for a given H 20
4.3.2 Optimize H for a given W 22
4.4 Comparison with DSNMF 25

5. Experimental Results 27
5.1 Database and Setting 27
5.2 Evaluation of Facial Representation 28
5.3 Effect of λI and λE 29
5.4 Comparison with Other Facial Representation Algorithms 30
5.5 Comparison with State-of-the-Art Recognition Algorithms 31

6. Limitation and Discussion 36

7. Conclusions 38

8. References 39


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