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研究生:曾銘崧
研究生(外文):Ming-Sung Tseng
論文名稱:熱感式指紋成像及其自動辨識法之研究
論文名稱(外文):Thermal Fingerprint Images and Their Automatic Recognition Techniques
指導教授:繆紹綱繆紹綱引用關係
指導教授(外文):Shaou-Gang Miaou
學位類別:碩士
校院名稱:中原大學
系所名稱:電子工程研究所
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:2001
畢業學年度:89
語文別:中文
論文頁數:78
中文關鍵詞:指紋辨識
外文關鍵詞:fingerprint identification
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隨著科技的日新月異,傳統使用來作為安全控管及身分辨識的方式已逐漸不符合時代的需求。因此近代發展出利用生物特徵作為系統管理的識別工具,且已成為一種方便又有效的安全控管方式,這其中又以指紋驗證最具有實用性與代表性。
本論文所研究的是有別於光學式的熱感式指紋。在熱感式指紋辨識技術核心演算法上,本論文主要是探討指紋重建、登入與特徵比對等三大部分。在指紋重建方面,主要是以最小絕對平均誤差求出指紋片段之間的關係性,以達到消除冗餘性之目的,並討論與分析不同的堆疊方式所造成之影響﹔在登入方面,本論文提出以片段與片段之間的冗餘程度訂出登入準則的概念;在特徵比對方面,除了嘗試核心點搜尋演算法以及結構比對演算法外,還提出一個改良式結構比對演算法的架構。
從實驗結果中可獲知,重建出的影像在清晰度與穩定度上皆優於從熱感式伴隨展示之軟體中所擷取的指紋影像;本論文所提出之改良式架構,在方向角不準確或者指紋影像整體縮放的情況下,皆能有效的辨識成功。
關鍵字:生物特徵、認證、指紋辨識、重建、特徵比對、冗餘性
With the rapid development of science and technology, the traditional method for security management and personal identification is gradually obsolete. Therefore, modern biometrics has been developed as an identification tool for system management, and it has become a convenient and effective security control measure. Among all possible ways of biometrics, fingerprint verification is the most practical and representative.
The thesis studies the thermal instead of optical fingerprints. In the kernel algorithm of thermal fingerprint identification techniques, this thesis will focus on the study of fingerprint image reconstruction, enrollment and minutiae matching. For the fingerprint reconstruction, the minimum absolute average error is applied to exploit the correlation between fingerprint slices so that the redundancy among slices can be removed. In addition, different slice stacking methods and their effects are discussed and analyzed. For the enrollment, the idea of establishing the enrollment criterion according to the degree of redundancy between slices is proposed. For the minutiae matching, we not only investigate the core finding and the structural matching algorithms, but also present an improved framework of the structural matching algorithm. In terms of clarity and stability, images reconstructed are superior to those obtained by the demonstration software associated with the thermal sensor. The improved framework can effectively identify the fingerprint even when the orientation is not correct or the fingerprint image is scaled globally.
Keywords: Biometrics, Verification, Fingerprint identification, Reconstruction, Minutiae matching, Redundancy
摘 要…………………………………………………………………………I
Abstract………………………………………………………………………II
致 謝…………………………………………………………………………III
目 錄…………………………………………………………………………IV
圖索引…………………………………………………………………………VII
表索引…………………………………………………………………………XI
第一章 緒論……………………………………………………………………1
1-1 生物特徵技術……………………………………………………………1
1-2 研究動機與目標…………………………………………………………4
1-3 內容大綱…………………………………………………………………5
第二章 熱感式指紋辨識原理與方法…………………………………………6
2-1 熱感式指紋辨識系統……………………………………………………6
2-2 感測器工作原理…………………………………………………………9
2-3 熱感式指紋辨識所遭遇的問題…………………………………………12
第三章 片段指紋之重建與登入準則之建立…………………………………15
3-1 片段指紋影像之分析……………………………………………………16
3-2 片段指紋重建之方法一…………………………………………………18
3-2.1 搜尋有效片段………………………………………………………18
3-2.2 重建演算法…………………………………………………………19
3-2.3 有效指紋區…………………………………………………………22
3-3 片段指紋重建之方法二…………………………………………………24
3-3.1 演算法流程圖………………………………………………………24
3-3.2 順向重建演算法與逆向重建演算法之比較………………………26
3-4 指紋邊界…………………………………………………………………27
3-5 去除雜點與毛邊…………………………………………………………28
3-6 登入準則之建立…………………………………………………………29
第四章 特徵比對………………………………………………………………31
4-1 特徵比對之演算法………………………………………………………32
4-2 對齊式比對演算法………………………………………………………33
4-3 核心點搜尋比對法………………………………………………………34
4-3.1 Gabor Features and the Proposed Method……………………34
4-3.2 Local Ridge Orientation ………………………………………36
4-3.3 Core Point Detection……………………………………………36
4-4 結構比對演算法…………………………………………………………37
4-4.1 第一級比對…………………………………………………………37
. 4-4.2 第二級比對…………………………………………………………40
4-5 改良式結構比對演算法…………………………………………………41
4-5.1 影像比例縮放的現象………………………………………………41
4-5.2 改良式結構比對演算法……………………………………………42
4-5.3 改良式結構比對的特色……………………………………………45
第五章 實驗模擬結果…………………………………………………………46
5-1 指紋重建與登入之實驗模擬結果………………………………………46
5-1.1 片段指紋重建之方法一的實驗……………………………………46
5-1.2 片段指紋重建之方法二的實驗……………………………………50
5-1.3 指紋邊界之實驗結果………………………………………………51
5-1.4 以濾波器去除雜點與毛邊之實驗結果……………………………57
5-1.5 方法一以及方法二之比較…………………………………………58
5-2 核心點搜尋之實驗結果…………………………………………………59
5-2.1 最佳 值之選取……………………………………………………59
5-2.2 最佳 值之選取……………………………………………………60
5-2.3 對指紋角度位移之探討……………………………………………61
5-3 結構比對之實驗結果……………………………………………………63
5-3.1 第一組實驗結果……………………………………………………63
5-3.2 第二組實驗結果……………………………………………………66
5-3.3 錯誤指紋比對之實驗………………………………………………68
5-4 改良式結構比對之實驗結果……………………………………………71
5-4.1 第一組實驗結果……………………………………………………71
5-4.2 第二組實驗結果……………………………………………………73
5-4.3 錯誤指紋比對之實驗………………………………………………74
第六章 結論與展望……………………………………………………………75
參考文獻………………………………………………………………………76
作者簡歷………………………………………………………………………78
[1] 張正弘、林群雄、鄭旭良與賴承澤,生物認證簡介與應用,國立中央大學資訊工程研究所,圖形識別實驗室,影像與識別, vol. 5, no. 4, pp. 36-46, 1999.[2] The Biometrics Report, SJB Services, pp. 47, 1995.[3] A. R. Rao, A Taxonomy for Texture Description and Identification. New York : Springer-Verlag, 1990.[4] http://www.gausscom.com.tw/ACR02spc.htm[5] http://www.majortek.com.tw/Html/fp.htm[6] K. Jain, S Pankanti, L. Hong, and R. Bolle, “An identity-authentication system using fingerprints,” Proc. of the IEEE, vol. 85, no. 9, pp. 1364-1388, 1997.[7] D. Maio, and D. Maltoni, “Neural network based minutiae filtering in fingerprints,” Proc. Fourteenth Int. Conf. on Pattern Recognition, vol. 2, pp. 1654-1658, 1998.[8] C. J. Lee and S. D. Wang, “Fingerprint feature extraction using Gabor filters,” electronics letters, vol. 35, no. 4, pp. 288-290, February 18th, 1999.[9] D. Maio and D. Maltoni, “Direct gray-scale minutiae detection in fingerprints,” IEEE Trans. Pattern Anal. Machine Intell., vol. 19, no. 1, pp. 27-39, 1997.[10]林韋成、呂立偉、劉書維與黃闞立,指紋辨識,國立台灣大學資訊工程學系專題研究報告。[11]D. K. Isonor and S. G. Zaky, “Fingerprint identification using graph matching,” Pattern Recognition, vol. 19, no. 2, pp. 13-122, 1986.[12]W. K. Lee and J. H. Chung, “Fingerprint recognition algorithm development using directional information in wavelet transform domain, ” IEEE International Symposium on Circuits and Systems, pp. 9-12, June 1997.[13]A. K. Hrechak and J. A. Mchugh, “Automated fingerprint recognition using structural matching, ” Pattern Recognition, vol. 23, no. 8, pp. 893-904, 1990.[14]L. C. Jain, “An automated matching technique for fingerprint identification,” Proc. First Int. Conf. on Knowledge-Based Intelligent Electronic Systems, pp. 21-23, May 1997.[15]A. Wahab, S. H. Chin and E. C. Tan, “Novel approach to automated fingerprint recognition,” IEE Proc.-Vis. Image Signal Process, vol. 145, no. 3, pp. 160-166, June 1998. [16]THOMSON-CSF SEMICONDUCTEURS SPECIFIQUES: FingerChipFC15A140.[17]J. F. Mainguet, M. P. John and B. Harris, “Fingerprint recognition based on silicon chips,” Future Generation Computer System, vol. 16, pp. 403-415, 2000. [18]M. Kawagoe and A. Tojo, “Fingerprint pattern classification,” Pattern Recognition, vol. 17, no. 3, pp. 295-303, 1984.[19]N. K. Ratha, K. Karu, S. Chen, and A. K. Jain, “A real-time matching system for large fingerprint databases,” IEEE Trans. Pattern Anal. Machine Intell., vol. 18, no. 8, pp. 799-813, August 1996. [20]C. J. Lee and S. D. Wang, “A Gabor filter-based approach to fingerprint recognition,” Proc. IEEE Workshop on Signal Processing Systems, pp. 371-378, 1999.[21]M. Kawagoe and A. Tojo, “Fingerprint pattern classification,” Pattern Recognition, vol. 17, no. 3, pp. 295-303, 1984.[22]V. S. Srinivasan and N. N. Murthy, “Detection of singular points in fingerprint images,” Pattern Recognition, vol. 25, no. 2, pp. 139-153, 1992.[23]S. H. Chang, F. H. Cheng, W. S. Hsu and G. Z. Wu, “Fast algorithm for point pattern matching : invariant to translations, rotations and scale changes,” Pattern Recognition, vol. 30, no. 2, pp. 311-320, 1997.
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