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Seminar internasional Universitas Negeri Surakarta
- 1. Iris Segmentation and Normalization Algorithm Based on Zigzag Collarette M. Rizky Faundra Mathematics Department Post Graduate Student Sepuluh Nopember Institut of Technology Surabaya Presented on ICSAS Syariah Hotel, Solo, 19th November 2016
- 3. What is Biometrics System? Biometrics system is self-recognition technology using body parts or human behavior
- 4. Biometrics Technology Biometrics technology has grown rapidly this decade. Some biometrics technology such as used as a self-replacing passwords at the important place that requires a high security
- 5. Comparison of Biometrics Biometrics Universality Distinctiveness Permanence Collectability Iris High High High Medium Fingerprints Medium High High Medium Voice Medium Low Low Medium Signature Low Low Low High Face High Low Medium High Palm Medium High High Medium
- 6. Application of Biometrics Method Coded Pattern Misidentificati on Rate Security Applications Iris Iris Pattern 1/1.200.000 High High Security Fingerprints Finger Pattern 1/1.000 Medium Universal Voice Voice Characteristics 1/30 Low Telephone Service Signature Shape of letters, writing order, pen pressure 1/100 Low Low Security Face Outline, shape & distribution of eyes, nose 1/100 Low Low Security Palm Size, length & thickness hands 1/700 Low Low Security
- 7. Iris Recognition Diagram Image Acquisition Iris Segmentation Normalization Eye Image Iris Region Feature Matching IrisTemplates Database Feature points in the iris region Feature Encoding IrisTemplate Identify or Reject Subject
- 8. Focus Research Image Acquisition Iris Segmentation Normalization Eye Image Iris Region Feature Encoding Feature Matching IrisTemplates Database Feature points in the iris region IrisTemplate Identify or Reject Subject
- 9. Iris Segmentation The method segmentation in iris has role important to the next process, selection of part of the iris is very important because it determines the percentage of the accuracy in iris recognition system. Method Selection Part of the iris Masek Full iris selection, by eyelid and eyelash Zhou Select 1750 , 2250 and 3100 , 3600 Kazakov Select interval 0, 450 ∪ 1050 , 2250 ∪ 3150 , 3600
- 10. Zigzag Collarette Area Zigzag collarette area of the iris is located near pupil, this area has many complex iris pattern and concentric with the pupil. Zigzag collarette areaPupil
- 11. Segmentation Diagram 1. Edge Pupil Detection Convert to binary image Sobel Operator Grouping at an angle Nonmaxsup Hystheresis
- 12. Finding Circle Pupil 2. Finding Circle Pupil CIRCULAR HOUGH TRANSFORM Centre of Pupil: 135, 182 Radius: 38
- 13. Isolation Zigzag Collarette Closing pixel outside Draw circle zigzag Limiting only Zigzag area Close pupil and eyelashes Close eyelid
- 14. Normalization Iris Since variations in the eye like pupil dilation and the inconsistence iris normalization is needed. In this experiment used the formulation Daugman Rubber Sheet Model to map the zigzag collarette into polar form as follows: 𝐼 𝑥 𝑟, 𝜃 , 𝑦(𝑟, 𝜃) → 𝐼(𝑟, 𝜃) with 𝑥 𝑟, 𝜃 = 1 − 𝑟 𝑥 𝑝 𝜃 + 𝑟𝑥 𝑧 𝜃 𝑦 𝑟, 𝜃 = 1 − 𝑟 𝑦𝑝 𝜃 + 𝑟𝑦𝑧 𝜃 where 𝑥 𝑝 𝜃 is the coordinates of the x-axis pupil boundary 𝑦𝑝 𝜃 is the coordinates of the y-axis pupil boundary 𝑥 𝑧 𝜃 is the coordinates of the x-axis zigzag collarette boundary 𝑦𝑧 𝜃 is the coordinates of the y-axis zigzag collarette boundary
- 15. Normalization Iris (Cont..) Daugman Rubber Sheet Model In Zigzag Collarete Area
- 16. Experiment Result The proposed iris segmentation and normalisation algorithm has been calculated and accuracy with specific threshold level to reduce edge near pupil. The experimental result is shown in Table 3 below: Threshold Level Accuracy 0.2 0.3 0.4 0.5 88.88% 98.88% 97.78% 73.33% Table 3. Accuracy of Specific Threshold Level
- 17. Conclusion 1. Canny edge detection and Hough transform can be localized pupil near zigzag area very well then Daugman Rubber Sheet Model can represent isolation zigzag collarette. 2. The experiment result show that algorithm can reduce edge near pupil with high performance of 98.88 percent with specific threshold level is 0.3. 3. Next experiment we will study about feature encoding and matching to get whole accuracy iris recognition system
- 18. References [1] Anil K. Jain, Sharath Pankanti and Salil Prabhakar, “Biometric Recognition: Security and Privacy Concerns”, IEEE Security & Privacy, 2003. [2] Li Ma, Yunhong Wang, Tieniu Tan, “Iris Recognition Based on Multichannel Gabor Filter”, ACCV2002: The 5th Asian Conference on Computer Vision, 2002. [3] Adler F. H., “Physiology of the Eye: Clinical Application”, The C.V. Mosby Company, 1965. [4] John Daugman, ”New Methods in Iris Recognition”, IEEE Transactions On Systems, Man, And Cybernetics—Part B: Cybernetics, Vol. 37, No. 5, 2007 [5] Lijun Zhou, Yide Ma, Jing Lian and Zhaobin Wang, “A New Effective Algorithm for Iris Location”, Proceeding of the IEEE International Conference on Robotics and Biometrics (ROBIO), Shenzhen, China, 2013. [6] Todor Kazakov, “Iris Detection and Normalization”, University of Birmingham Thesis, 2011. [7] John Canny, “A Computational Approach to Edge Detection”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, PAMI-8(6):679–698, 1986. [8] P.V.C. Hough, “Method and Means for Recognizing Complex Patterns”, U.S. Patent 3069654, 1962. [9] CASIA iris image database.<http: //www.cbsr.ia.ac.cn/IrisDatabase>. [10] Himansu Rai, “Iris Recognition Using Combined Support Vector Machine and Hamming Distance Approach”. Expert Systems with Applications 41 (2014) 588–593, 2014.
- 19. TERIMA KASIH