以多分數加權融合方式進行虹膜影像品質檢定

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：57

、訪客IP：3.149.255.10

姓名

阮勻(JUAN, YUN) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

以多分數加權融合方式進行虹膜影像品質檢定
(Iris Image Quality Assessment Using Weighted Multiple Score Fusion)

相關論文

★ 基於虹膜色彩空間的極端學習機的多類型頭痛分類	★ 基於深度學習之工業用智慧型機器視覺系統：以文字定位與辨識為例
★ 基於深度學習的即時血壓估測演算法	★ 基於深度學習之工業用智慧型機器視覺系統:以焊點品質檢測為例
★ 基於pix2pix深度學習模型之條件式虹膜影像生成架構	★ 以核方法化的相關濾波器之物件追蹤方法實作眼動儀系統
★ 雷射都普勒血流原型機之驗證與校正	★ 以生成對抗式網路產生特定目的影像—以虹膜影像為例
★ 一種基於Faster R-CNN的快速虹膜切割演算法	★ 運用深度學習、支持向量機及教導學習型最佳化分類糖尿病視網膜病變症狀
★ 應用卷積神經網路的虹膜遮罩預估	★ Collaborative Drama-based EFL Learning with Mobile Technology Support in Familiar Context
★ 可用於自動訓練深度學習網路的網頁服務	★ 基於深度學習方法之高精確度瞳孔放大片偵測演算法
★ 基於CNN方法之真假人臉識別模型	★ 深度學習基礎模型與自監督學習

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]

至系統瀏覽論文 ( 永不開放)

摘要(中)

虹膜辨識是一種以擷取虹膜特徵並加以比對以得出該虹膜所屬者身分的生物辨識方法。然而，虹膜影像的品質會對比對的結果有著各式的影響。如果不將品質不佳的影像排除，虹膜特徵的比對結果會受到影像模糊、反光點、眼皮等等影響而導致虹膜辨識的結果發生誤判。因為使用者經常會在實時虹膜辨識系統中提供不盡理想的虹膜影像或是照明設備曝光不足、過度曝光，誤判的情形將會更為嚴重。
　　本研究為了解決虹膜影像品質過差造成誤判的影響，測試了多種虹膜影像品質檢定方式並挑選出其中多個合適的檢定方式以在合理的時間內篩選出品質良好的影像。為了使多種虹膜影像品質檢定方式都能夠同時發揮功效，本研究嘗試了相加式品質計算、相乘式品質計算以及加權式品質計算於中國科學院虹膜資料庫(CASIA-Iris-Thousand V4)之中，並以相等錯誤率(Equal Error Rate)作為虹膜影像品質計算方式表現的評斷標準。本研究最終以加權過後的相乘式品質計算排除一半低於門檻值的影響後將相等錯誤率由3.1244%降至0.9092%。

摘要(英)

Iris recognition is one of a biometric recognition method in which we extract iris features and compare them with other irises to identify whom the iris belong to. However, the performance of iris recognition is highly related to the quality of iris images. If images with poor quality are not excluded, the false non-matching of iris may happen because of images blur, reflections, or eyelids occlusion and the like. The problem becomes more evident in a real-time iris recognition system where users often provide non-ideal iris images, or when the iris images are underexposed or overexposed.
To tackle this problem, in this dissertation we experimented multiple iris quality assessment algorithms and proposed feasible methods to select images with good quality in a reasonable time frame. Furthermore, large-scaled experiments are performed using CASIA-Iris-Thousand V4 database in order to figure out the optimized method for score fusion, for the goal of minimizing the equal error rate (EER) in the iris matching experiments. After the experiment, we used one of the best method (Exponentially Weighted quality assessment algorithm) to filter out half of the images. After such iris image quality thresholding, we are able to lower the EER of the large-scale iris recognition (with remaining iris images) from 3.1244% to 0.9092%, which proves the effectiveness of the proposed iris image quality assessment algorithm.

關鍵字(中)

★ 影像品質檢定
★ 虹膜辨識

關鍵字(英)

★ Image Quality Assessment
★ Iris Recognition

論文目次

Chinese Abstract i
English Abstract ii
Acknowledgement iv
Table of Contents v
List of Figures vii
List of Tables ix
Explanation of Symbols x
Chapter 1 Introduction 1
1-1 Research Background 1
1-2 Research Purpose 1
1-3 Literature Review 2
1-4 Thesis Structure 3
Chapter 2 Iris Recognition And Iris Image Quality Assessment 4
2-1 Steps of Iris Recognition 4
2-1-1 Iris Segmentation 4
2-1-2 Iris Normalization 5
2-1-3 Iris Feature Extraction 5
2-1-4 Iris Mask Generation 6
2-1-5 Iris Feature Matching 6
2-2 Iris Quality Assessment Algorithms 6
2-2-1 Generic Image Quality Assessment Algorithms 7
1. Sharpness 7
(1) Gradient of Images 8
(2) Sharpness Measurement Purposed by ISO/IEC 29794-6:2015 8
(3) Sharpness Measurement Purposed by Daugman 8
2. Degree of Motion Blur 9
3. Gray Scale Utilization 10
2-2-2 Iris Usability Assessment Algorithms 11
1. Usable Iris Area 12
2. Contrast of Iris Texture 12
3. Iris Radius 13
4. Pupil Dilation 14
5. Iris Pupil Concentricity 14
2-3 Normalization of Different Quality Assessment Algorithms 16
2-4 Metric on Quality Assessment Algorithms 17
Chapter 3 Experiment Setup 19
3-1 Experiment Database 19
3-2 Baseline Experiment Result 19
Chapter 4 Individual Iris Quality Assessment Algorithms Experiment Results 21
4-1 Generic Image Quality Assessment Algorithms Experiment Results 22
4-1-1 Sharpness 22
4-1-2 Degree of Motion Blur 24
4-1-3 Gray Scale Utilization 24
4-2 Iris Usability Assessment Algorithms Experiment Results 25
4-2-1 Usable Iris Area 25
4-2-2 Contrast of Iris Texture 27
4-2-3 Iris Radius 28
4-2-4 Pupil Dilation 30
4-2-5 Iris Pupil Concentricity 32
4-3 Summary of Individual Iris Quality Assessment Algorithms 34
Chapter 5 Combined Iris Quality Assessment Algorithms Experiment Results 37
5-1 Experimental Results of Iris Quality Score Fusion using Addition 37
5-2 Experimental Results of Iris Quality Score Fusion using Multiplication 41
5-3 Experimental Results of Iris Quality Score Fusion using Different Weighting Methods 45
5-4 Summary of Combined Iris Quality Assessment Algorithms 52
Chapter 6 Discussion And Conclusion 54
Reference 56
Appendix I EER of Different Combination of Added Algorithms 58
Appendix II EER of Different Combination of Multiplied Algorithms 67

參考文獻

[1] J. Daugman, “Probing the Uniqueness and Randomness of IrisCodes: Results from 200 Billion Iris Pair Comparisons”, Proceedings of the IEEE, Vol 94, Issue 11, pp. 1927-1935, IEEE, November 2006.
[2] J. Daugman, “Information Theory and the IrisCode”, IEEE Transactions on Information Forensics and Security, Vol 11, Issue 2, pp. 400-409, IEEE, February 2016.
[3] J. Daugman and C. Downing, “Searching for Doppelgängers: Assessing the Universality of the IrisCode Impostors Distribution”, IET Biometrics, Vol 5, Issue 2, pp. 65-75, IET, May 2016.
[4] J. Daugman, “Recognizing Iris Texture by Phase Demodulation”, IEE Colloquium on Image Processing for Biometric Measurement, pp. 2/1-2/8, London, UK, 1994.
[5] J. Daugman, “High Confidence Recognition of Persons by Iris Patterns”, Proceedings IEEE 35th Annual 2001 International Carnahan Conference on Security Technology, pp. 254-263, London, UK, October 2001.
[6] J. Daugman, “How Iris Recognition Works”, IEEE Transactions on Circuits and Systems for Video Technology, Vol 14, Issue 1, pp. 21-30, January 2004.
[7] J. Daugman, “New Methods in Iris Recognition”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol 37, Issue 5, pp. 1167-1175, January 2007.
[8] R. Szewczyk et al. “A Reliable Iris Recognition Algorithm Based on Reverse Biorthogonal Wavelet Transform”, Pattern Recognition Letters, Vol 33, Issue 8, pp 1019-1026, Elsevier, June 2012.
[9] H. J. Santos-Villalobos et al., “ORNL Biometric Eye Model for Iris Recognition”, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems, pp. 176-182, Arlington, VA, USA, September 2012.
[10] A. Radman et al., “Fast and Reliable Iris Segmentation Algorithm”, IET Image Processing, Vol 7, Issue 1, pp. 42-49, IET, March 2013.
[11] A. Hilal et al., “Elastic Strips Normalisation Model for Higher Iris Recognition Performance”, IET Biometrics, Vol 3, Issue 4, pp. 190-197, IET, December 2014.
[12] C. Shi and L. Jin, “A Fast and Efficient Multiple Step Algorithm of Iris Image Quality Assessment”, 2010 2nd International Conference on Future Computer and Communication, Vol 2, pp. 589-593, Wuha, China, May 2010.
[13] X. Liu et al., “Can No-Reference Image Quality Metrics Assess Visible Wavelength Iris Sample”, 2017 IEEE International Conference on Image Processing, pp. 3530-3534, Beijing, China, September 2017.
[14] M. Jenadeleh et al., “Realtime Quality Assessment of Iris Biometrics Under Visible Light”, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 556-565, Salt Lake City, Utah, USA, June 2018.
[15] International Standard ISO/IEC 29794-6: Biometric Sample Quality – Part 6: Iris Image Data, 2015.
[16] Chinese Academy of Sciences, CASIA-Iris-Thousand database, http://biometrics.idealtest.org/index.jsp.

指導教授

栗永徽(Li, Yung-Hui)

審核日期

2019-7-24

推文