A Robust Eyelashes and Eyelid Detection in Transformation Invariant Iris Recognition: In Application with LRC Security System

Biometric authentication is an essential task for any
kind of real-life applications. In this paper, we contribute two
primary paradigms to Iris recognition such as Robust Eyelash
Detection (RED) using pathway kernels and hair curve fitting
synthesized model. Based on these two paradigms, rotation invariant
iris recognition is enhanced. In addition, the presented framework
is tested with real-life iris data to provide the authentication for
LRC (Learning Resource Center) users. Recognition performance
is significantly improved based on the contributed schemes by
evaluating real-life irises. Furthermore, the framework has been
implemented using Java programming language. Experiments are
performed based on 1250 diverse subjects in different angles of
variations on the authentication process. The results revealed that the
methodology can deploy in the process on LRC management system
and other security required applications.

Authors:

• R. Bremananth

Keywords:

• Authentication
• biometric
• eye lashes detection
• iris scanning
• LRC security
• secure access.

References:

[1] Bremananth R, Chitra A, ”New methodology for a person identification
system,” Springer - Sadhana, vol. 31, part 3, pp. 259-276, June 2006.
[2] J. Daugman, ”New methods in iris recognition,” IEEE Trans. Syst., Man,
Cybern., Part B: Cybern., vol. 37, no. 5, pp. 1167-1175, 2007.
[3] Daugman J, ”How iris recognition works,” IEEE Trans. Circuits Syst.
Video Technol. vol. 14, pp. 21-30, 2004.
[4] J. Daugman, ”The importance of being random: Statistical principles of
iris recognition,” Pattern Recognit., vol. 36, no. 2, pp. 279-291, 2003.
[5] Li Ma, T. Tan, Y. Wang, and D. Zhang, ”Efficient iris recognition by
characterizing key local variations,” IEEE Trans. Image Process., vol.13,
no. 6, pp. 739-750, 2004.
[6] A. Picon, O. Ghita, P. Whelan, and P. Iriondo, ”Fuzzy spectral and
spatial feature integration for classification of nonferrous materials in
hyperspectral data,” IEEE Trans. Ind. Inform., vol. 5, no. 4, pp. 483-494,
Nov. 2009.
[7] A. Kumar, ”Computer-vision-based fabric defect detection: A survey,”
IEEE Trans. Ind. Electron., vol. 55, no. 1, pp. 348-363, 2008.
[8] D. Tsai and J. Luo, ”Mean shift-based defect detection in
multicrystalline solar wafer surfaces,” IEEE Trans. Ind. Inform., vol.
7, no. 1, pp. 125-135, Feb. 2011.
[9] Q. Zhao, D. Zhang, L. Zhang, and N. Luo, ”Adaptive fingerprint pore
modeling and extraction,” Pattern Recogn., vol. 43, no. 8, pp.2833-2844,
2010.
[10] B. Kang and K. Park, ”A robust eyelash detection based on iris
focus assessment,” Pattern Recognit. Lett., vol. 28, no. 13, pp.
1630-1639,2007.
[11] Z. He, T. Tan, Z. Sun, and X. Qiu, ”Toward accurate and fast iris
segmentation for iris biometrics,” IEEE Trans. Pattern Anal. Mach.
Intell., pp. 1670-1684, 2008.
[12] H. Proena, ”Iris recognition: Analysis of the error rates regarding the
accuracy of the segmentation stage,” Image and Vision Comput., vol.
28, no. 1, pp. 202-206, 2010.
[13] Bremananth R, ”Transformation Invariance and Luster Variability in
the Real-Life Acquisition of Biometric Patterns,” International Journal
of Computer Science and Information Security, vol. 9, No.11, 2011,
pp.8-15, 2011. ISSN: 1947-5500:USA, 2011.
[14] Bremananth R and Chitra A, ”Rotation Invariant Recognition of Iris,”
Journal of Systems Science and Engineering, SSI, vol.17, no.1, pp.69-78,
June 2008 (ISSN: 0972-5032).
[15] Yulin Si, Jiangyuan Mei, and Huijun Gao,”Novel Approaches to Improve
Robustness, Accuracy and Rapidity of Iris Recognition Systems,” IEEE
Trans. On Indu. Informatics, vol.8, No. 1, Feb 2012.
[16] Donold Hearn and M. Pauline Baker,“Computer Graphics C Version,”
Second edition, Published by Prentice Hall, 1997.