RECOGNITION OF IRIS & PALM-PRINT FOR SECURITY OF USER IDENTIFICATION

Prof. Chhaya Gochade, Ajay Rajput, Vaibhav Daundkar

Abstract


Palm-print and Iris are the effective methodology for personal authentication. Biometrics is a field of analysis of biological characteristics of an individual. Its purpose is to recognize and automatically verify the identity of a person based on physiological or behavioral characteristics. Multi-modal biometrics which combining several biometric based systems, are increasingly studied. Indeed, they reduce some limitations of unimodal based biometric systems, such as the inability to acquire data of individuals or intentional fraud, while improving the recognition performance. These benefits of multi-modality with unimodal biometric based systems are obtained by fusing multiple biometric traits.

                       In this system, combining palm-print and Eye-Iris modalities to get the best of both worlds. They are recent and very important biometrics modalities due to their discriminatory power, robustness over time and there acceptability by users. To use, there characteristics must be extracted and enrolled for future comparison. The present proposed method is based on Histogram of Oriented Gradients (HOG) and Random Forest Transform (RFT) in order to improve the performance of the multi-modal biometric system based on palm-print and iris scan modalities. The results of the different classifiers are combined (fused) at the matching score level.

Keywords


Palm-Print, Biometric, Iris, Normalization, Authentication, HOG, RFT.

Full Text:

PDF

References


J. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 15, pp. 1148–1161, 1993.

R.Wildes, “Iris recognition: an emerging biometric technology,” Proc. IEEE, vol. 85, pp. 1348–1363, 1997.

K. Bowyer, K. Hollingsworth, and P. Flynn, “Image understanding for iris biometrics: A survey,” Image Vision Comput., vol. 110, pp. 281–307, 2008.

Z. Sun and T. Tan, “Ordinal measures for iris recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, pp. 2211–2226, 2009.

A. Kumar and A. Passi, “Comparison and combination of iris matchers for reliable personal authentication,” Pattern Recognit., vol. 43, pp. 1016–1026, 2010.

J. Daugman and I. Malhas, “Iris recognition border-crossing system in the uae,” International Airport Review, vol. 8, pp. 49–53, 2004.

J. Daugman, “600 million citizens of India are now enrolled with biometric id,” SPIE Newsroom, 2014.

A. Kong, D. Zhang and G. Lu, “A study of identical twins palm print for personal verification”, Pattern Recognition, vol. 39, no. 11, pp. 2149-2156, 2006.

L. Wang, “Some issues of biometrics: technology intelligence, progress and challenges”, IJITM, Inderscience publisher, Vol. 11. 1/2, pp. 72-72, 2012.

Dalal Navneet, and Bill Triggs, “Histograms of oriented gradients for human detection”, Computer Visio.

Breiman Leo, “Random forests”, Machine learning, Vol. 45.1, pp. 5-32, 2001.

Poh Norman, and Josef Kittler, “Multimodal information fusion”, Multimodal signal processing theory and applications for human computer interaction, pp. 153, 2010.


Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2017 INTERNATIONAL EDUCATION AND RESEARCH JOURNAL