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研究生:施尚佑
研究生(外文):Shang-You Shi
論文名稱:在圖論空間中使用區域邊際強化型分類器的人臉辨識系統
論文名稱(外文):Face Recognition Using Local Margin-Enhanced Classifier in Graph-Based Space
指導教授:連震杰
指導教授(外文):Jenn-Jier Lien
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
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:2009
畢業學年度:97
語文別:英文
論文頁數:43
中文關鍵詞:人臉辨識外來者拒絕區域邊際強化的空間最近鄰居單人多張人臉影像辨識
外文關鍵詞:K-Nearest NeighborLocal margin-enhanced spaceFace recognitionImposterImage set recognitionRejection
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在人臉辨識(Face Recognition)系統中很少提及判斷外來者(Imposter)的機制,如果沒有事先判斷此人是否為外來者,那麼無論人臉辨識系統的準確度再高,也無法辨識正確。針對這個問題,本研究發展拒絕機制,使系統在人臉辨識的同時並判斷此人是否為外來者。這個機制是運作於所提出的區域邊際強化的空間 (Local margin-enhanced space);訓練資料於該區域邊際強化的空間中,不僅能保留資料的區域鑑別性(Local Discriminant Structure),且空間是依據k-NN分類器(k-nearest neighbor classifier)的分類規則設計,因此在此設計空間下能提升系統的辨識能力。不僅如此,透過在空間中每一筆資料與其最近鄰居的(Nearest Neighbor)距離,可設計出以機率模型為主的拒絕外來者機制,並使用延伸過的k-最近距離分類器, (Extended k-NN classifier),使得我們的人臉辨識/拒絕系統不僅可以做單人單張人臉影像的辨識也可做單人多張人臉影像辨識。當在單人多張人臉影像的時候,能提供優良的人臉辨識效果與拒絕外來者的能力。
For face recognition system, the rejection mechanism is usually ignored, i.e. if the testing person is not in the training set (where we call it “imposter” here), no matter how outstanding the face recognition system is, the imposter won’t be able to be recognized with right label and it will cause high false alarm rate. In order to solve this problem, we propose a rejection mechanism and thus the recognition system incorporating the rejection mechanism not only can classify each test data but also has the ability to reject the imposter data. Moreover, the Local Margin-Enhanced Space is proposed in order to design the rejection mechanism feasibly, where not only the local discriminant data structure can be preserved but the local margin of each data is enhanced based on the k-nearest neighbor classification rule. Hence, the statistic rejection mechanism can be designed by modeling the acceptance and rejection likelihood probabilities according to the distance of each data and its corresponding nearest neighbor in the LME space. Finally, the performance of the proposed system is evaluated using the challenging databases. The results not only demonstrate the system to recognize the single image or image sets (multiple faces) with a high degree of accuracy, but also perform a promising result with 81% rejection rate.
TABLE OF CONTENTS
CHAPTER PAGE
LIST OF FIGURES ............................................................................................................. VII
LIST OF TABLES ................................................................................................................ IX
CHAPTER 1. Introduction .................................................................................................... 1
CHAPTER 2. System Flowchart ........................................................................................... 4
CHAPTER 3. Local Margin Enhancement for Nearest Neighbor (NN) Classification ........ 7
3.1. Graph-based Subspace Creation Using Local Sensitive Discriminant Analysis ............ 8
3.1.1. Dimensions reduction by Graph Embedding .............................................. 9
3.1.2. Graph Embedding with Linear Transformation LSDA ............................. 10
3.2. Local Margin-Enhanced Space Creation Using LMNN ...................................... 12
CHAPTER 4. Acceptance-Rejection Likelihood Modeling for Subject Classification ...... 16
4.1. Probability of Acceptance and Rejection ............................................................. 16
4.2. Statistical Modeling of Acceptance and Rejection Subject Class ........................ 19
CHAPTER 5. Test Procedure .............................................................................................. 25
CHAPTER 6. Experimental Results ................................................................................... 27
6.1. Database and Image Preprocessing ...................................................................... 27
6.2. Recognition Results .............................................................................................. 28
6.2.1. Comparison of Subspace Learning Methods ............................................ 28
6.2.2. Subspace Analysis with Various Dimensions ............................................ 29
6.3. Rejection and Recognition Evaluation based on One and Set Images ................. 30
6.3.1 Rejection Performance Evaluation on Different Spaces ............................ 31
6.3.2 Recognition/ Rejection Evaluation on LME Space .................................... 32
6.3.3 Performance Evaluation Using Probability Ratio and 1NN Distance ........ 39
CHAPTER 7. Conclusion ................................................................................................... 41
References ........................................................................................................................... 42
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