臺灣博碩士論文加值系統

在一個多生物特徵的驗證系統中，需要一個有效地融合方法來整合從多個單一生物特徵系統中所得到的資訊。本論文觀察了sum rule-based 融合方法和支援向量機(Support Vector Machines, SVM)-based融合方法在評分階段的效能。在這邊使用了三種生物特徵：指紋、人臉以及指靜脈。並且提出一個由min-max正規化所推導出之較健全的正規化方法。在四個不同的多生物特徵資料庫中實驗後，顯示出我們提出的方法結合sum rule-based融合方法和SVM-based融合方法可獲得相當高的正確性。在經由本論文提出的正規化方法再進行簡單的sum rule後的效能可以和其他基於比對分數密度之估測的方法來比較。比較sum rule-based融合方法和SVM-based融合方法的實驗結果發現，當核心以及參數被仔細地選取時，SVM-based融合方法較sum rule-based融合方法可以獲得更好的效果。

In a multimodal biometric system, the effective fusion method is necessary for combining information from various single modality systems. In this paper we examined the performance of sum rule-based score level fusion and Support Vector Machines (SVM)-based score level fusion. Three biometric characteristics were considered in this study: fingerprint, face, and finger vein. We also proposed a new robust normalization scheme which is derived from min-max normalization scheme. Experiments on four different multimodal databases suggest that integrating the proposed scheme in sum rule-based fusion and SVM-based fusion leads to consistently high accuracy. The performance of simple sum rule preceded by our normalization scheme is comparable to another approach which is based on the estimation of matching scores densities. Comparison between experimental results on sum rule-based fusion and SVM-based fusion reveals that SVM-based fusion could attain better performance compared to sum rule-based fusion, provided that the kernel and its parameters have been carefully selected.

Abstract i
摘要 ii
Acknowledgements iii
Table of Contents v
List of Equations vii
List of Figures viii
List of Tables ix
I Introduction 1
I.1 Multimodal Biometric System 2
I.2 Objectives 5
I.3 Thesis Organization 7
II Score Level Fusion 8
II.1 Various Normalization Schemes 9
II.1.1 Min-Max Normalization 10
II.1.2 Z-Score Normalization 11
II.1.3 Tanh-Estimators Normalization 12
II.1.4 Reduction of High-scores Effect Normalization 13
II.2 Sum Rule-based Fusion 15
II.3 Support Vector Machines (SVM)-based Fusion 16

III Databases and Experimental Design 18
III.1 Databases 18
III.2 Experimental Design 22
IV Experimental Results 25
IV.1 Performance of Sum Rule-based Fusion 25
IV.2 Performance of SVM-based Fusion 31
V Conclusions 35
References 37
Appendix: Tanh Normalization Example 41

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