การวิเคราะห์หาค่า Threshold ที่เหมาะสมและปลอดภัยสำหรับการยืนยันตัวตนผ่านม่านตา
Article Sidebar
เผยแพร่แล้ว:
มี.ค. 22, 2021
คำสำคัญ:
การยืนยันตัวตน, ม่านตา, ค่า Threshold, Circle Hough Transform
Main Article Content
บทคัดย่อ
บทคัดย่อ
การยืนยันตัวตนเป็นกระบวนการเบื้องต้นของการรักษาความปลอดภัยทางเทคโนโลยีสารสนเทศ ปัจจุบันระบบ Biometric นิยมนำมาใช้เพื่อการยืนยันตัวตน เพราะมีความปลอดภัยสูง โดย Biometric ในส่วนของม่านตาถือว่ามีความ ปลอดภัยสูง อย่างไรก็ตามค่า Threshold ซึ่งเป็นเกณฑ์เพื่อการยืนยันตัวตน ยังไม่มีงานวิจัยที่ศึกษาอย่างละเอียด โดยค่า Threshold คือค่าที่กำหนดจากจำนวนเปอร์เซ็นที่ยอมรับของค่าที่ถูกต้องหรือที่ตรงกันระหว่าง Iris ที่อยู่ในฐานข้อมูลกับ Iris ที่นำมายืนยันตัวตน ดังนั้นจุดประสงค์หลักของงานวิจัยนี้ คือการวิเคราะห์หาค่า Threshold ที่เหมาะสมและปลอดภัย เพื่อการยืนยันตัวตนจาก Iris โดยการตรวจสอบภาพดวงตาใช้เทคนิค Circle Hough Transform และภาพดวงตาที่ใช้ในงานวิจัยมาจาก CASIA V.4 และในส่วนของการวัดประสิทธิภาพ ได้แก่ค่า TP, ค่า FP และค่า FN ผลการวิจัย พบว่าค่า Threshold 72.9246 เปอร์เซ็นต์มีค่า TP 77.50, FP 22.50 และ FN 1.00 ซึ่งเป็นค่าที่มีความเหมาะสมและปลอดภัย
Article Details
บท
บทความวิจัย
References
[1] R. Shay et al., “Encountering Stronger Password Requirements: User Attitudes and Behaviors,” in Proceedings of the Sixth Symposium on Usable Privacy and Security, New York, NY, USA, 2010, p. 2:1–2:20.
[2] L. Leng and J. Zhang, “Dual-key-binding cancelable palmprint cryptosystem for palmprint protection and information security,” J. Netw. Comput. Appl., vol. 34, no. 6, pp. 1979–1989, Nov. 2011.
[3] S. F. Darwaish, E. Moradian, T. Rahmani, and M. Knauer, “Biometric Identification on Android Smartphones,” Procedia Comput. Sci., vol. 35, pp. 832–841, 2014.
[4] S. Garg, A. Kumar, and M. Hanmandlu, “Biometric authentication using finger nail surface,” in 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA), 2012, pp. 497–502.
[5] K. Nandakumar, A. K. Jain, and S. Pankanti, “Fingerprint-Based Fuzzy Vault: Implementation and Performance,” IEEE Trans. Inf. Forensics Secur., vol. 2, no. 4, pp. 744–757, Dec. 2007.
[6] A. Nagar, K. Nandakumar, and A. K. Jain, “A hybrid biometric cryptosystem for securing fingerprint minutiae templates,” Pattern Recognit. Lett., vol. 31, no. 8, pp. 733–741, Jun. 2010.
[7] Z. Jorgensen and T. Yu, “On Mouse Dynamics As a Behavioral Biometric for Authentication,” in Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security, New York, NY, USA, 2011, pp. 476–482.
[8] K. Miyazawa, K. Ito, T. Aoki, K. Kobayashi, and H. Nakajima, “An Effective Approach for Iris Recognition Using Phase-Based Image Matching,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 10, pp. 1741–1756, Oct. 2008.
[9] Verma, P., Dubey, M., Basu, S., & Verma, P., “Hough transform method for iris recognition: A biometric approach,” Int J Eng Inn Technol, vol. 1, pp. 43–48.
[10] F. Jan, I. Usman, and S. Agha, “Reliable iris localization using Hough transform, histogram-bisection, and eccentricity,” Signal Process., vol. 93, no. 1, pp. 230–241, Jan. 2013.
[11] F. Jan, I. Usman, S. A. Khan, and S. A. Malik, “Iris localization based on the Hough transform, a radial-gradient operator, and the gray-level intensity,” Opt. - Int. J. Light Electron Opt., vol. 124, no. 23, pp. 5976–5985, Dec. 2013.
[12] P. K. Pallav and S. R. Ganorkar, Investigation and Analysis of Hough-DCT- Hamming Distance Based Method of Iris Recognition. .
[13] T.-H. Min and R.-H. Park, “Eyelid and eyelash detection method in the normalized iris image using the parabolic Hough model and Otsu’s thresholding method,” Pattern Recognit. Lett., vol. 30, no. 12, pp. 1138–1143, Sep. 2009.
[14] J. Illingworth and J. Kittler, “A survey of the hough transform,” Comput. Vis. Graph. Image Process., vol. 44, no. 1, pp. 87–116, Oct. 1988.
[15] “Biometrics ideal test/CASIA Iris Image Database Version 4.0,” 23-Oct-2016.
[16] New Methods in Iris Recognition. .
[17] E. Mohammadi Arvacheh, “A Study of Segmentation and Normalization for Iris Recognition Systems,” 2006.
[18] J. Daugman, “How iris recognition works,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 21–30, Jan. 2004.
[19] A. Bouridane, “Recent Advances in Iris Recognition: A Multiscale Approach,” in Imaging for Forensics and Security, Springer US, 2009, pp. 49–77.
[20] “Iris Recognition: On the Segmentation of Degraded Images Acquired in the Visible Wavelength - Semantic Scholar.” [Online]. Available: /paper/Iris-Recognition-On-the-Segmentation-of-Degraded-Proença/a3fcf3d32a5a4fcc83 027e3d367ecc0df3ec4f64. [Accessed: 06-Feb-2017].
[21] N. Cherabit, F. Z. Chelali, A. Djeradi, N. Cherabit, F. Z. Chelali, and A. Djeradi, “Circular Hough Transform for Iris localization,” Sci. Technol., vol. 2, no. 5, pp. 114–121, 2012.
[22] Y. Desmedt, “Threshold cryptosystems,” in Advances in Cryptology — AUSCRYPT ’92, 1992, pp. 1–14.
[23] L. Birgale and M. Kokare, Iris Recognition Without Iris Normalization 1. .
[24] H. Proenca and L. A. Alexandre, “Iris Recognition: An Analysis of the Aliasing Problem in the Iris Normalization Stage,” in 2006 International Conference on Computational Intelligence and Security, 2006, vol. 2, pp. 1771–1774.
[25] M. Vatsa, R. Singh, and A. Noore, “Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing,” IEEE Trans. Syst. Man Cybern. Part B Cybern., vol. 38, no. 4, pp. 1021–1035, Aug. 2008.
[26] T. Tan, Z. He, and Z. Sun, “Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition,” Image Vis. Comput., vol. 28, no. 2, pp. 223–230, Feb. 2010.
[27] M. T. Khan, D. Arora, and S. Shukla, “Feature extraction through iris images using 1-D Gabor filter on different iris datasets,” in 2013 Sixth International Conference on Contemporary Computing (IC3), 2013, pp. 445–450.
[2] L. Leng and J. Zhang, “Dual-key-binding cancelable palmprint cryptosystem for palmprint protection and information security,” J. Netw. Comput. Appl., vol. 34, no. 6, pp. 1979–1989, Nov. 2011.
[3] S. F. Darwaish, E. Moradian, T. Rahmani, and M. Knauer, “Biometric Identification on Android Smartphones,” Procedia Comput. Sci., vol. 35, pp. 832–841, 2014.
[4] S. Garg, A. Kumar, and M. Hanmandlu, “Biometric authentication using finger nail surface,” in 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA), 2012, pp. 497–502.
[5] K. Nandakumar, A. K. Jain, and S. Pankanti, “Fingerprint-Based Fuzzy Vault: Implementation and Performance,” IEEE Trans. Inf. Forensics Secur., vol. 2, no. 4, pp. 744–757, Dec. 2007.
[6] A. Nagar, K. Nandakumar, and A. K. Jain, “A hybrid biometric cryptosystem for securing fingerprint minutiae templates,” Pattern Recognit. Lett., vol. 31, no. 8, pp. 733–741, Jun. 2010.
[7] Z. Jorgensen and T. Yu, “On Mouse Dynamics As a Behavioral Biometric for Authentication,” in Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security, New York, NY, USA, 2011, pp. 476–482.
[8] K. Miyazawa, K. Ito, T. Aoki, K. Kobayashi, and H. Nakajima, “An Effective Approach for Iris Recognition Using Phase-Based Image Matching,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 10, pp. 1741–1756, Oct. 2008.
[9] Verma, P., Dubey, M., Basu, S., & Verma, P., “Hough transform method for iris recognition: A biometric approach,” Int J Eng Inn Technol, vol. 1, pp. 43–48.
[10] F. Jan, I. Usman, and S. Agha, “Reliable iris localization using Hough transform, histogram-bisection, and eccentricity,” Signal Process., vol. 93, no. 1, pp. 230–241, Jan. 2013.
[11] F. Jan, I. Usman, S. A. Khan, and S. A. Malik, “Iris localization based on the Hough transform, a radial-gradient operator, and the gray-level intensity,” Opt. - Int. J. Light Electron Opt., vol. 124, no. 23, pp. 5976–5985, Dec. 2013.
[12] P. K. Pallav and S. R. Ganorkar, Investigation and Analysis of Hough-DCT- Hamming Distance Based Method of Iris Recognition. .
[13] T.-H. Min and R.-H. Park, “Eyelid and eyelash detection method in the normalized iris image using the parabolic Hough model and Otsu’s thresholding method,” Pattern Recognit. Lett., vol. 30, no. 12, pp. 1138–1143, Sep. 2009.
[14] J. Illingworth and J. Kittler, “A survey of the hough transform,” Comput. Vis. Graph. Image Process., vol. 44, no. 1, pp. 87–116, Oct. 1988.
[15] “Biometrics ideal test/CASIA Iris Image Database Version 4.0,” 23-Oct-2016.
[16] New Methods in Iris Recognition. .
[17] E. Mohammadi Arvacheh, “A Study of Segmentation and Normalization for Iris Recognition Systems,” 2006.
[18] J. Daugman, “How iris recognition works,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 21–30, Jan. 2004.
[19] A. Bouridane, “Recent Advances in Iris Recognition: A Multiscale Approach,” in Imaging for Forensics and Security, Springer US, 2009, pp. 49–77.
[20] “Iris Recognition: On the Segmentation of Degraded Images Acquired in the Visible Wavelength - Semantic Scholar.” [Online]. Available: /paper/Iris-Recognition-On-the-Segmentation-of-Degraded-Proença/a3fcf3d32a5a4fcc83 027e3d367ecc0df3ec4f64. [Accessed: 06-Feb-2017].
[21] N. Cherabit, F. Z. Chelali, A. Djeradi, N. Cherabit, F. Z. Chelali, and A. Djeradi, “Circular Hough Transform for Iris localization,” Sci. Technol., vol. 2, no. 5, pp. 114–121, 2012.
[22] Y. Desmedt, “Threshold cryptosystems,” in Advances in Cryptology — AUSCRYPT ’92, 1992, pp. 1–14.
[23] L. Birgale and M. Kokare, Iris Recognition Without Iris Normalization 1. .
[24] H. Proenca and L. A. Alexandre, “Iris Recognition: An Analysis of the Aliasing Problem in the Iris Normalization Stage,” in 2006 International Conference on Computational Intelligence and Security, 2006, vol. 2, pp. 1771–1774.
[25] M. Vatsa, R. Singh, and A. Noore, “Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing,” IEEE Trans. Syst. Man Cybern. Part B Cybern., vol. 38, no. 4, pp. 1021–1035, Aug. 2008.
[26] T. Tan, Z. He, and Z. Sun, “Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition,” Image Vis. Comput., vol. 28, no. 2, pp. 223–230, Feb. 2010.
[27] M. T. Khan, D. Arora, and S. Shukla, “Feature extraction through iris images using 1-D Gabor filter on different iris datasets,” in 2013 Sixth International Conference on Contemporary Computing (IC3), 2013, pp. 445–450.