跳到主要內容

簡易檢索 / 詳目顯示

研究生: 安晨碩
CHEN-SHUO AN
論文名稱: 結合紋理特徵和幾何特徵的指紋影像品質評估
Fingerprint Image Quality Evaluation Combining Texture and Geometric features
指導教授: 陳慶瀚
口試委員:
學位類別: 碩士
Master
系所名稱: 資訊電機學院 - 資訊工程學系在職專班
Executive Master of Computer Science & Information Engineering
論文出版年: 2016
畢業學年度: 104
語文別: 中文
論文頁數: 66
中文關鍵詞: 指紋影像紋理特徵品質評估機率神經網路
相關次數: 點閱:15下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 指紋影像評估主要應用於指紋感測器的影像品質評估。一個設計良好的指紋影像評估系統同時有助於提升指紋註冊的正確性,並有效提升指紋辨識率。本研究提出了一個結合紋理特徵分析及機率神經網路(Probability Neural Network,PNN)的指紋影像評估系統,以便改良現有的指紋影像品質評估方法。本研究的指紋影像評估流程可分為紋理特徵分析,PNN神經網路分類,整合PNN分類結果與傳統NFIQ評估,得到最終品質分數。就實驗結果顯示,結合紋理特徵分析的指紋影像評估可以提升評估指紋品質時的精確度。 


    Fingerprint image assessment is primarily applied to assess the quality of images stored in fingerprint readers. A satisfactorily designed fingerprint image assessment system effectively improves the accuracy of fingerprint registration and the rate of fingerprint recognition. This study incorporated a new fingerprint image assessment system that combines fingerprint texture analysis with a probability neural network (PNN) to improve the existing fingerprint image quality assessment method. The fingerprint image assessment procedure involved texture analysis and PNN neural network classification. Subsequently, the PNN classification results were organized and compared to the conventional National Institute of Standards and Technology fingerprint image quality assessment results to obtain the final quality scores. According to the result of the experiment, the fingerprint image assessment system that incorporated texture analysis improved the accuracy of fingerprint image quality assessment.

    摘要 i ABSTRACT ii 誌謝 iii 目錄 iv 圖目錄 vi 表目錄 viii 第一章、緒論 1 1.1 研究背景 1 1.2 研究目的 3 1.3 論文架構 3 第二章、文獻回顧 4 2.1 簡介 4 2.2 NFIQ指紋影像品質的定義 4 2.3 計算指紋影像品質 4 2.3.1 特徵擷取 5 2.3.2 評估指紋特徵點品質: 8 2.3.3 特徵向量 8 2.3.4 訓練神經網路 10 2.4 評估指紋影像品質分數 10 2.5 紋理分析 10 2.6 PNN機率神經網路 12 第三章、指紋影像品質評估方法設計 17 3.1 MIAT方法論 17 3.2 IDEF0 18 3.3 Grafcet離散事件建模 19 3.4 指紋影像品質評估架構 22 3.5 指紋影像品質評估設計 25 3.5.1紋理特徵品質評估 26 3.5.2品質計算 27 第四章、實驗 28 4.1 實驗環境 28 4.2 指紋影像品質評估平台 32 4.2.1指紋影像品質評估平台實作 32 4.2.2 指紋影像品質評估程序 35 4.3指紋影像品質評估實驗 37 4.4品質與辨識性能的相關性分析 42 第五章、結論與未來方向 50 5.1 結論 50 5.2 未來方向 51 參考文獻 52

    [1] 中華民國內政部移民署. 入出國自動查驗通關系統. [Online]. http://www.immigration.gov.tw/lp.asp?ctNode=36092&CtUnit=19627&BaseDSD=7&mp=1

    [2] Biometrics Overview. [Online]. http://www.biometrics.gov/

    [3] University of Toronto. (2011, May) A Global Overview Of Digital Wallet Technologies. [Online]. http://propid.ischool.utoronto.ca/digiwallet_overview

    [4] Hailong Jia, Kun Cao, "The research on the preprocessing algorithm for fingerprint image," Electrical & Electronics Engineering (EEESYM), pp. 163-166, June 2012.
    [5] Roli Bansal, Priti Sehgal and Punam Bedi, "Minutiae Extraction from Fingerprint Images - a Review," IJCSI International Journal of Computer Science Issues, vol. 8, no. 3, Sep. 2011.
    [6] Jain, A.K. Prabhakar, S. Hong, L. and Pankanti, S, "Filterbank-based fingerprint matching - Image Processing," IEEE Transactions on, vol. 9, pp. 846-859, May 2000.
    [7] Oltmans, E., van Diessan, R.J. , van Wijngaarden, H., "Preservation functionality in a digital archive," Digital Libraries, 2004. Proceedings of the 2004 Joint ACM/IEEE Conference on, pp. 279-286, June 2004.
    [8] X. Cheng, A. Bradley and L. N. Thibos, "Predicting subjective judgment of best focus with objective image quality metrics," Journal of Vision(2004), pp. 279-286, Apr. 2004.
    [9] Ramesh Jain, Rangachar Kasturi, Brian G. Schunck, MACHINE VISION.: McGraw-Hill, Inc., 1995.
    [10] National Institute of Standards and Technology. [Online]. http://www.nist.gov/

    [11] Biometric Quality. [Online]. http://www.nist.gov/itl/iad/ig/bio_quality.cfm

    [12] FVC2006: the Fourth International Fingerprint Verification Competition. [Online]. http://bias.csr.unibo.it/fvc2006/

    [13] J.L. Blue and P.J. Grother, "Training feed forward networks using conjugate gradients," NISTIR 4776 and in Conference on Character Recognition and Digitizer technologies, vol. 1661, pp. 179-190, Feb. 1992.
    [14] D. Liu and J. Nocedal, "On the limited memory BFGS method for large scale optimization," Imathematical programming B, no. 45, pp. 503-528, 1989.
    [15] O.M. Omidvar and C. L. Wilson, "Information content in neural net optimization," NISTIR 4766, and in Journal of connection science, vol. 6, pp. 91-103, 1993.
    [16] Zhan Shuanghuan, Zhang Hongbin, Image Texture Energy-Entropy-Based Blind Steganalysis., 2007.
    [17] A. Gebejes, R. Huertas, Texture Characterization based on Grey-Level Co-occurrence Matrix., 2007.
    [18] M. Farrokhrooz, A performance comparison between Conventional PNN and Multi-spread PNN in ship noise classification., 2006.
    [19] Jing Peng, On Parzen windows classifiers., 2014.
    [20] H. Chen, C. M. Kuo, C. Y. Chen, and J. H. Dai, "The design and synthesis using hierarchical robotic discrete-event modeling," Journal of Vibration and Control, vol. 19, pp. 1603-1613, 2013.
    [21] H. Chen, T. K. Yao, J. H. Dai and C. Y. Chen, "A pipelined multiprocessor SOC design methodology for streaming signal processing," Journal of Vibration and Control, vol. 20, pp. 163-178, 2014.
    [22] R. J. Mayer, IDEF0 Function Modeling.: Air force Systems Command.
    [23] GTS-511E2. [Online]. http://www.adh-tech.com.tw/?33,gts-511e2

    [24] FingerCell. [Online]. http://fingerprint-it.com/_sol_Fingercell.html

    [25] Mohan Sridharan, Bayesian Methods for Data Analysis in Software Engineering., 2010.

    QR CODE
    :::