融合掌紋及掌背血管影像之生物認證系統

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：15

、訪客IP：18.119.138.196

姓名

王世宏(Shih-Hung Wang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

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)

相關論文

★ 使用視位與語音生物特徵作即時線上身分辨識	★ 以影像為基礎之SMD包裝料帶對位系統
★ 手持式行動裝置內容偽變造偵測暨刪除內容資料復原的研究	★ 基於SIFT演算法進行車牌認證
★ 基於動態線性決策函數之區域圖樣特徵於人臉辨識應用	★ 基於GPU的SAR資料庫模擬器：SAR回波訊號與影像資料庫平行化架構 (PASSED)
★ 利用掌紋作個人身份之確認	★ 利用色彩統計與鏡頭運鏡方式作視訊索引
★ 利用欄位群聚特徵和四個方向相鄰樹作表格文件分類	★ 筆劃特徵用於離線中文字的辨認
★ 利用可調式區塊比對並結合多圖像資訊之影像運動向量估測	★ 彩色影像分析及其應用於色彩量化影像搜尋及人臉偵測
★ 中英文名片商標的擷取及辨識	★ 利用虛筆資訊特徵作中文簽名確認
★ 基於三角幾何學及顏色特徵作人臉偵測、人臉角度分類與人臉辨識	★ 一個以膚色為基礎之互補人臉偵測策略

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 掌紋
★ 掌背血管
★ 影像融合

關鍵字(英)

論文目次

Contents i
List of Figures iii

CHAPTER 1 INTRODUCTION 1
1.1 Motivation 1
1.2 Survey of Related Works 2
1.2.1 Biometric Recognition System Using Palm print and Palm vein Images 2
1.2.2 Image Fusion about Biometric Recognition System 4
1.3 System Overview 5
1.4 Organization of the Thesis 6

CHAPTER 2 PREPROCESSING 7
2.1 Palm Region Segmentation 7
2.2 Finger-webs Locating 14
2.3 ROI Locating 16

CHAPTER 3 IMAGE FUSION BASED ON DISCRETE WAVELET TRANSFORM 20
3.1 Introduction to Discrete Wavelet Transform 20
3.2 Wavelet-Based Image Fusion of Palm print and Palm vein 23

CHAPTER 4 FEATURE EXTRACTION 29
4.1 Sobel Operators 29
4.2 Local Binary Pattern 30
4.3 Local Derivative Pattern 31
4.4 Histogram of Iterative Thresholding 34

CHAPTER 5 CLASSIFICATION METHOD 36
5.1 Template Matching 36
5.1 Support Vector Machine 37

CHAPTER 6 EXPERIMENTAL RESULTS 40
6.1 Database Description 40
6.2 Experimental Results 41

CHAPTER 7 CONCLUSIONS AND FUTURE WORKS 49
7.1 Conclusions 49
7.1 Future Works 50

REFERENCE 51

參考文獻

[1] A. K. Jain, R. Bolle and S. Pankanti, “Biometrics Personal identification in network society”, Kluwer Academic Publishers, 1999.

[2] D. S. Huang, W. Jia, and D. Zhang, “Palmprint verification based on principal lines”, Pattern Recog., vol. 41, no. 4, pp. 1316-1328, 2008.

[3] C. C. Han, P. C. Chang, and C. C. Hsu,. “Personal identification using hand geometry and palm-print,”. Fourth Asian Conference on Computer Vision (ACCV),pp.747-752,2000.

[4] D. Zhang and W. Shu, ”Two novel characteristics in palm printverification: datum point invariance and line feature matching”,Pattern Recognition, vol. 32, pp. 691-702, 1999.

[5] G. Lu, D. Zhang and K. Wang, “Palmprint recognition using eigenpalms features”, Pattern Recognition Letters, vol. 24, issue 9-10, pp. 1463-1467, June, 2003.

[6] A. Kong and D. Zhang, “Competitive coding scheme for palmprint verification,” in ICPR, pp. 520–523, 2004.

[7] Z. Sun, T. Tan, Y. Wang, and S. Z. Li, “Ordinal palmprint represention for personal identification,” in CVPR, pp. 279–284, 2005.

[8] W. K. Kong and D. Zhang,. “Palmprint texture analysis based on low-resolution images for personal authentication,”. 16th International Conference on Pattern Recognition, vol. 3 , pp.807 .810, 2002.

[9] Y. Zhang, D. Sun, Z. Qiu, “Hand-based single sample biometric recognition”, Neural Computer and Application, 2011.

[10] C. L. Lin, H. Y. Cheng, K. C. Fan, C. W. Lu, C. J. Juan and C.W. Kuo, “Bi-feature verification for palmprint images captured in peg-less scenarios,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 27, no. 5, Aug. 2013.

[11] C. W. Lu, C. L. Lin, K. C. Fan, H. Y. Cheng and C. J. Juan, “Palmprint Verification for Images Captured in Peg-less Scenarios,” Applied Mechanics and Materials, vol. 284-287, pp. 3178-3183, 2013

[12] C. L. Lin, C. C. Thomas, K. C. Fan. "Palmprint verification using hierarchical decomposition." Pattern Recognition vol. 38, no.12, pp. 2639-2652, 2005.

[13] C. C. Han, H. L. Cheng, C. L. Lin and K. C. Fan, “Personal Authentication Using Palmprint Features”, Pattern Recognition, vol. 36, issue 2, pp. 371-381, 2003.

[14] P. MacGregor and R. Welford, “Veincheck: imaging for security and personnel identification”, Adv. Imaging, vol. 6, pp. 52-56, 1991.

[15] S. K. Im, H. M. Park, S. W. Kim, C. K. Chung and H. S. Choi, .”Improved vein pattern extracting algorithm and its implementation,”. Consumer Electronics, 12000.ICCE. 2000 Digest of Technical Paper International Conference on, pp. 2~3, 2000.

[16] L. Mirmohamadsadeghi, and A. Drygajlo, “Palm vein recognition with Local Binary Patterns and Local Derivative Patterns”, International Joint Conference on Biometrics, pp. 1-6, 2011.

[17] C. L. Lin and K. C. Fan, “Biometric verification using thermal images of palm-dorsa vein patterns”, presented at IEEE Trans. Circuits Syst. Video Techn., pp.199-213, 2004.

[18] M. Shahin, A. Badawi, and M. Kamel, ”Biometric Authentication Using Fast Correlation of Near Infrared Hand Vein Patterns”, International Journal of Biological and Medical Sciences, vol 2, No.1, pp. 141-148, 2007.

[19] J. G. Wang, W. Y. Yau, A. Suwandy, and E. Sung, “Person recognition by fusing palmprint and palm vein images based on "Laplacianpalm" representation”, Pattern Recognition, pp.1514-1527, 2008.

[20] Liu, Z., Y. Yin, H. Wang, S. Song and Q. Li, 2010, “Finger vein recognition with manifold learning”. J. Network Computer Applications, pp. 275-282, 2010.

[21] J. Wang, W. Yau, A. Suwandy, and E. Sung, “Fusion of palmprintand palm vein images for person recognition based on laplacianpalm feature,” in CVPR, pp. 1–8, 2007.

[22] D. R. Kisku, A. Rattani, P. Gupta & J. K. Sing, “Biometric sensor image fusion for identity verification: A case study with wavelet-based fusion rules graph matching”. In:Technologies for Homeland Security, 2009. HST′09. IEEE Conference on. IEEE, pp. 433-439, 2009.

[23] M. Sonka, V. Hlavac and R. Boyle, “Image Processing, Analysis, and Machine Vision”, Second edition, PWS publishing, New York, 1999.

[24] J. G. et al.,”Mathematical Morphology and Tts Applications to Image and Signal Processing”, Kluwer Academic Publishers, 2000.

[25] T. Stathaki.: “Image fusion – algorithms and applications,” Academic Press, 2008

[26] G. Pajares, and J. Manuel de la Cruz. "A wavelet-based image fusion tutorial." Pattern recognition, vol. 37, issue 9, pp. 1855-1872, September, 2004.

[27] K. S. Fu, R. C. Gonzalez and C. S. G. Lee, Robotics:Control, Sensing, Vision, and Intelligence, pp.426-427. McGraw-Hill, New York, 1987

[28] B. Zhang, Y. Gao, S. Zhao & J. Liu, “Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor” .Image Processing, IEEE Transactions on, vol. 19, issue 2, pp. 533-544, 2010.

[29] M. Sonka, V. Hlavac and R. Boyle, “Image Processing, Analysis, and Machine Vision”, Second edition, PWS publishing, New York, 1999.

[30] Vladimir N. Vapnik, “The Nature of Statistical Learning Theory”, Springer, 1995.

[31] C. C. Chang and C. J. Lin, “LIBSVM: A Library for Support Vector Machines”, 2001.
Available: http://www.csie.ntu.edu.tw/verb~cjlin/libsvm

[32] R. Sanchez-Reillo, C. Sanchez-Avila and A. Gonzalez-Macros, “Biometric identification through hand geometry measurements”, IEEE Trans. Pattern Anal. Mach. Intell. 22(2000) 168_1171.

[33] D. S. Huang, W. Jia and D. Zhang, “Palmprint verification based on principal lines”, Pattern Recogn. 41 (2008) 1316_1328.

[34] D. Zhang and W. Shu, “Two novel characteristics in palmprint verification:Datum point invariance and line feature matching”, Pattern Recogn. 32 (1999) 691_702.

[35] J. You, W. Li and D. Zhang, Hierarchical palmprint identification via multiple feature extraction, Pattern Recogn. 35 (2002) 847_859.

[36] G. Lu, D. Zhang and K. Wang, Palmprint recognition using eigenpalms features, Pattern Recogn. Lett. 24 (2003) 1463_1467.

[37] D. Zhang and W. Shu, “Two novel characteristics in Palmprint verification: Datum point invariance and line feature matching”, Pattern Recognition, vol. 32, pp. 691-702, 1999.

[38] X. Pan and Q. Ruan, “Palmprint recognition with improved two-dimensional locality preserving projections”, presented at Image Vision Comput., pp. 1261-1268, 2008.

[39] X. Zhou, Y. Peng and M. Yang, “Palmprint Recognition Using Wavelet and Support Vector Machines”, in Proc. PRICAI, pp. 385-393, 2006.

指導教授

范國清、林志隆

審核日期

2014-8-28

推文