Commenced in January 2007
Paper Count: 32020
A Robust Eyelashes and Eyelid Detection in Transformation Invariant Iris Recognition: In Application with LRC Security System
Authors: R. Bremananth
Abstract: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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1129700Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 885
 Bremananth R, Chitra A, ”New methodology for a person identification system,” Springer - Sadhana, vol. 31, part 3, pp. 259-276, June 2006.
 J. Daugman, ”New methods in iris recognition,” IEEE Trans. Syst., Man, Cybern., Part B: Cybern., vol. 37, no. 5, pp. 1167-1175, 2007.
 Daugman J, ”How iris recognition works,” IEEE Trans. Circuits Syst. Video Technol. vol. 14, pp. 21-30, 2004.
 J. Daugman, ”The importance of being random: Statistical principles of iris recognition,” Pattern Recognit., vol. 36, no. 2, pp. 279-291, 2003.
 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.
 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.
 A. Kumar, ”Computer-vision-based fabric defect detection: A survey,” IEEE Trans. Ind. Electron., vol. 55, no. 1, pp. 348-363, 2008.
 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.
 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.
 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.
 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.
 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.
 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.
 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).
 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.
 Donold Hearn and M. Pauline Baker,“Computer Graphics C Version,” Second edition, Published by Prentice Hall, 1997.