Abstract

This paper a new biometric method for personal identification is been presented by iris identification of a person in lower dimensionality and reduced template size than the other previous approaches in 2D Eigen space, so that it can be use for verification in application areas . Here the iris images are expressed in lower dimension, re-tending its features by using covariance matrix and Eigen matrix to a covariant-Eigen space vector. The proposed approach is also suitable to work on half iris image. The proposed approach shows high accurate result.

References

• Bertillon, A, 1885. La couleur de Piris. Revue scientifique. Vol. 36, No. 3, 65-73.
• Daugman, J. 1992. High confidence personal identification by rapid video analysis of iris Texture. Proc. IEEE International Carnaha conf. on security technology, 50-60.
• Daugman, J. 1993. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 15, No. 11, 1148-1161.
• Daugman, J. 2004. How iris recognition works. Circuits and Systems for Video Technology. IEEE Trans. , Vol. 14, No. 1, 21-30.
• Daugman, J. 2007. New Methods in Iris Recognition. Proc. IEEE Trans. on system, man and cybernetics-part B, Vol. 37, No 5, 1167-1175.
• Savithiri, G. , and Murugan, A. 2011. Performance Analysis on Half Iris Feature Extraction using GW, LBP and HOG. International Journal of Computer Applications, Vol. 22, No. 2 (May 2011), 27-32.
• Hussain, Md. A. 2010. Eigenspace Based Accurate Iris Recognition System. Proc. Annual IEEE India Conference (INDICON), 763-767.
• Monro, D. M. , Rakshit, S. , and Zhang, D. 2007. DCT-Based Iris Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 29, No. 3 (April 2007), 586-595.
• Das, A. , and Parekh, R. 2012. Iris Recognition using a Scalar base Template in Eigen-space. IJCST, Vol. 3, No. 5, 74-79.
• Wang, Q, Zhang, X. , Li, M. , Dong, X. , Zhou, Q. , and Yin, Y. 2012. Adaboost and multi-orientation 2D Gabor-based noisy iris recognition. Pattern Recognition Letters, Vol. 33, 978–983.
• Roy, K. , and Bhattacharya, P. 2007. Iris Recognition Based on Zigzag Collarette Region and Asymmetrical Support Vector Machines. International Conference on Image Analysis and Recognition, 854–865.
• Wang, L. , Yang, G. , and Yin, Y. 2010. Fast Iris Localization Based on Improved Hough Transform. Rough Set and Knowledge Technology, 439–446.
• Nabti, M. and Bouridane, A. 2007. An Improved Iris Recognition System Using Feature Extraction Based on Wavelet Maxima Moment Invariants. International Conference on Biometrics, 988 – 996.
• Roy, K. , Bhattacharya, P. , and Ching, Y. S. 2011. Iris recognition using shape-guided approach and game theory. Pattern Anal Application, Vol. 14, 329–348.
• Radman, A. , Jumari, K. , and Zainal, N. 2011. Iris Segmentation: A Review and Research Issues. International Conference on Software Engineering and Computer Systems, 698–708.
• Woodard, D. L. , Pundlik, S. J. , Miller,P. E. , and Lyle, J. R. 2011. Appearance-based periocular features in the context of face and non-ideal iris recognition. Signal, Image and Video Processing, Vol. 5, 443–455.
• Luis Miguel Zamudio-Fuentes1, Mireya S. García-Vázquez, and Alejandro Alvaro Ramírez-Acosta 2011. Local Quality Method for the Iris Image Pattern. Computer Analysis of Images and Patterns, 79–88.
• Martinez, F. , Carbone, A. , and Pissaloux, E. 2011. Radial Symmetry Guided Particle Filter for Robust Iris Tracking. Computer Analysis of Images and Patterns, 531–539.
• Erdogmus, N. , and Dugelay, J. 2010. An Efficient Iris and Eye Corners Extraction Method. Structural, Syntactic, and Statistical Pattern Recognition, 549–558.
• Abidin, Z. Z. , Manaf, M. , and Shibghatullah, A. S. 2011. A New Model of Securing Iris Authentication Using Steganography. International Conference on Software Engineering and Computer Systems, 547–554.
• CASIA Iris Image Database Version 3. 0 (CASIA-IrisV3), Available: www. biometrics. idealtest. org