Iris Recognition using Four Level HAAR Wavelet Transform: A Literature review
International Journal of Electronics and Communication Engineering |
© 2016 by SSRG - IJECE Journal |
Volume 3 Issue 6 |
Year of Publication : 2016 |
Authors : Anjali Soni and Prashant Jain |
How to Cite?
Anjali Soni and Prashant Jain, "Iris Recognition using Four Level HAAR Wavelet Transform: A Literature review," SSRG International Journal of Electronics and Communication Engineering, vol. 3, no. 6, pp. 14-18, 2016. Crossref, https://doi.org/10.14445/23488549/IJECE-V3I6P106
Abstract:
There is considerable rise in the research of iris recognition system over a period of time. Most of the researchers has been focused on the development of new iris pre-processing and recognition algorithms for good quail iris images. In this paper, iris recognition system using Haar wavelet packet is presented. Wavelet Packet Transform (WPT ) which is extension of discrete wavelet transform has multiresolution approach. In this iris information is encoded based on energy of wavelet packets. And then matching of this iris code with the stored one is performed using hamming distance . Our proposed work significantly decreases FAR and FRR values as compared to previous work. Experimental results are demonstrating significant improvements in iris verification process.
Keywords:
Biometrics, Iris recognition, Iris segmentation, Iris normalization, Wavelet packet.
References:
[1] Ales Muron and Jaroslav Pospisil, “The human iris structure and its usages,” Acta Univ. Palacki. Olomuc. Fac. Rerum Nat. Phys., vol. 39, 2000, pp. 87–95.
[2] J.G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, Nov 1993, pp. 1148–1160.
[3] J.G. Daugman, “Demodulation by Complex-Valued Wavelets for Stochastic Pattern Recognition,” Int’l J. Wavelets, Multiresolution and Information Processing, vol. 1, no. 1, 2003, pp. 1-17.
[4] R. Wildes, “Iris Recognition: An Emerging Biometric Technology,” Proc. IEEE, vol. 85, 1997, pp. 1348-1363.
[5] S. Lim, K. Lee, O. Byeon, and T. Kim, “Efficient Iris Recognition through Improvement of Feature Vector and Classifier,” ETRI J, vol. 23, 2001, no. 2, pp. 61-70.
[6] Y. Zhu, T. Tan, and Y. Wang, “Biometric Personal Identification Based on Iris Patterns,” Proc. Int’l Conf. Pattern Recognition, vol. 2, 2000, pp. 805-808.
[7] L. Ma, Y. Wang, and T. Tan, “Personal identification based on iris texture analysis,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, Dec 2003, pp. 1519–1533.
[8] W. Boles and B. Boashash, “A human identification technique using images of the iris and wavelet transform,” IEEE Trans. on Signal Processing, vol. 46, no. 4, Apr 1998, pp. 1185–1188.
[9] C. Sanchez-Avilaa, R. Sanchez-Reillob, “Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation,” Pattern Recognition, vol. 38, 2005, pp. 231 – 240.
[10] K N Pushpalatha 1, Aravind Kumar Gautham2 D.R.Shashikumar3 and K.B ShivaKumar4 “ Iris Recognition System with Frequency Domain Features optimized with PCA and SVM Classifier” 5, No 1, September 2012.
[11] L. Ma, T. Tan, Y. Wang, and D. Zhang, “Efficient iris recognition by characterizing key local variations,” IEEE Trans. Image Process, vol. 13, 2004, pp. 739–750.
[12] L. Ma, T. Tan,D. Zhang, andY.Wang, “Local intensity variation analysis for iris recognition,” Pattern Recognition, vol. 37, no. 6, , 2005, pp. 1287–1298.
[13] Farid Benhammadi & Nassima Kihal “Personal Authentication Based on Iris Texture Analysis “2008 IEEE. [14] Tamililakkiya, V; Vani, K.; Lavanya, A.; Micheal A.; "Linear and non-linear feature extraction Algorithms for lunar images"; (SIPIj); 2011; IVSL.
[15] Gatheejathul Kubra.J, Rajesh.P , “ Iris Recognition and its Protection Overtone using Cryptographic Hash Function” SSRG International Journal of Computer Science and Engineering (SSRGIJCSE) – volume 3 Issue 5–May 2016