融合掌紋及掌背血管影像之生物認證系統;Biometric Verification System Using Fused Image of Palm Print and Palm-dorsa Vein

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Computer Science and Information Engineering > Electronic Thesis & Dissertation > Item 987654321/65825

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65825

Title:	融合掌紋及掌背血管影像之生物認證系統;Biometric Verification System Using Fused Image of Palm Print and Palm-dorsa Vein
Authors:	王世宏;Wang,Shih-Hung
Contributors:	資訊工程學系
Keywords:	掌紋;掌背血管;影像融合
Date:	2014-08-28
Issue Date:	2014-10-15 17:11:09 (UTC+8)
Publisher:	國立中央大學
Abstract:	以生物特徵作為身份認證系統逐漸受到重視，且在安全系統領域扮演了重要的角色。已經有許多生物特徵被廣泛的應用在個人認證系統，例如指紋、虹膜以及臉等。但是傳統的單一特徵身份認證系已經逐漸無法有所突破，而且反詐騙能力不足。相較之下，多特徵身份認證系統由於擷取了多重生物特徵，優點及實用性都高於單一特徵身份認證統。本系統採用了兩種生物特徵:掌紋及掌背血管。本篇論文首先應用一個方法擷取感興區塊，此方法可打破限制，利用非固定式的裝置收集掌紋及掌背血管影像。接著我們將掌紋及背血管進行影像融合，以得到比單張影像更豐富且有用的特徵。此外，我們利用新的特徵擷取方法來獲得影像中的特徵資訊。最後分別以樣板比對以及支援向量機兩種分類技術來區分是否為本人。實驗結果得到98. 73%的準確率，這說明了我們所提出的方法是有效果且具可靠性。;Biometric verification gradually plays an important role and highly demand for security systems. There are many biometric features including fingerprint, iris, hand geometry and facial image that can be used for biometric verification. However, the performance of traditional uni-model biometric systems can not meet the damand in providing satisfactory anti-spoofing capabilities. Multi-model biometric systems are then emerging with more satisfactory performance than uni-model biometric systems because multiple information can be acquired from different biometric characteristics. In this thesis, two physical biometric features including palm print and palm vein are utilized in our biometric verification system. In our approach, we devise a method to extract the region of interest (ROI) which relieves the limitations constrained by traditional docking devices. Then, palm print and palm-dorsa vein images are fused to form a new image for providing richer and more useful information. Next, the features adopted for verification are extracted by using Histogram Iterative Thresholding (HIT). Finally, template matching and support vector machine (SVM) are employed as the classifiers for identity verification. Experimental result shows that 98.73% accuracy rate can be achieved by using our proposed approach. It demonstrates that our proposed approach is efficient and robust in the application of biometric verification.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	583	View/Open

社群 sharing

Loading...