ALGORITHMS FOR BINARIZING, ALIGNING AND RECOGNITION OF FINGERPRINTS
A. Pillai, S. Mil'shtein, M. Baier
2011
Abstract
Minutiae based algorithms are widely used today for fingerprint authentication. In this study, we report the use of the Fast Fourier Transform (FFT) as a base principle for our recognition method, and have also developed image normalization methods. We also developed a novel method to align fingerprints to a common reference orientation based on the Fourier Mellin Transform. Two methods for image recognition are described. The first method uses image subtraction techniques in conjunction with a thresholding scheme. The second method, which is currently in development, utilizes multiple neural networks running in parallel. This technique is expected to be able to run image comparisons on large databases in real-time through the use of modern parallel processing technology. In this study we analyzed 720 fingerprints generated by wet-ink, flat digital scanners, and by a novel touch less fingerprinting scanner. For the image subtraction method comparing high quality fingerprints (prints taken in touch less way), the rate of success is 97%. For poorer quality prints, (those taken with wet-ink) the rate of success dropped to 93%. Recognition statistics are not currently available for the neural network based image recognition method as it is currently in development.
References
- Maltoni, D., Maio D., Jain A. K., Prabhakar S., 2003, Handbook of Fingerprint Recognition SpringerVerlag, New York,
- S. Pankanti, S. Prabhakar, A. K. Jain, CVPR 2001 Volume 1, 2001 pp I-805-I-812 vol.1. “On the individuality of fingerprints” Proc of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
- Anil Jain, Arun Ross, Salil Prabhakar, 2001, pp. 282-285, “Fingerprint Matching using Minutiae and Texture Features” Proc. of international conference on Image Processing.
- Palma J, Liessner C, Mil'shtein S, 2006, “Contactless Optical Scanning of Fingerprints with 180° View” Scanning, 28, 6, pp 301-304
- S. Mil'shtein, J. Palma, C. Liessner, M. Baier, A. Pillai, and A.Shendye, 2008, "Line Scanner for Biometric Applications" IEEE Intern. Conf. on Technologies for Homeland Security, ISBN 978-1-4244-1978-4 P 205- 208.
- P. Meenen, R. Adhami, 2005, “Approaches to image binarization in current automated fingerprint identification systems”, Proceedings of the ThirtySeventh Southeastern Symposium on System Theory, ISBN: 0-7803-8808-9
- C. Hu, J. Yin, E. Zhu, H. Chen, Y. Li, 2008, “Fingerprint Alignment using Special Ridges” ISBN 978-1-4244- 2175-6
- W. Chen, Y. Gao, 2007, “Minutiae-based Fingerprint Alignment Using Phase Correlation”, Mechatronics and Machine Vision in Practice, Springer Link, pp 193-198,
- C. Carvalho, H. Yehia, 2004, “Fingerprint Alignment using Line Segments”, Biometric Authentication, Springer, pp 1-10
- T. Zhang, J. Tian, Y. He, J. Cheng, X. Yang, 2003, “Fingerprint alignment using similarity histogram”, International conference on audio and video-based biometric person authentication, pp 854-861
- Xiaoxin Guo Zhiwen Xu Yinan Lu Yunjie Pang, Sep 2005, “An Application of Fourier-Mellin Transform in Image Registration”, The Fifth International Conference on Computer and Information Technology, pp 619-623.
- Masters, Timothy, 1993, “Practical Neural Network Recipies in C++” ISBN: 0-12-479040-2
- B. Gour, T.K. Bandopadhyaya, R. Patel, 2010, “ART and Modular Neural Network Architecture for multilevel Categorization and Recognition of Fingerprints”, 2010 Third International Conference on Knowledge Discovery and Data Mining, pp 536-539
- A. L. H. Jin, A. Chekima, J. A. Dargham, L. C. Fan, 2002, “Fingerprint Identification and Recognition using Backpropagation Neural Network”, Proceeding of Student Conference on Research and Development, pp 98-101
- B. Gour, et. Al, 2005, “Fast Fingerprint Identification System using Backpropagation Neural Network and Self Organizing Map”, Proc. Of Glow Gift., International Level Seminar.
Paper Citation
in Harvard Style
Pillai A., Mil'shtein S. and Baier M. (2011). ALGORITHMS FOR BINARIZING, ALIGNING AND RECOGNITION OF FINGERPRINTS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011) ISBN 978-989-8425-47-8, pages 426-432. DOI: 10.5220/0003362104260432
in Bibtex Style
@conference{visapp11,
author={A. Pillai and S. Mil'shtein and M. Baier},
title={ALGORITHMS FOR BINARIZING, ALIGNING AND RECOGNITION OF FINGERPRINTS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)},
year={2011},
pages={426-432},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003362104260432},
isbn={978-989-8425-47-8},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)
TI - ALGORITHMS FOR BINARIZING, ALIGNING AND RECOGNITION OF FINGERPRINTS
SN - 978-989-8425-47-8
AU - Pillai A.
AU - Mil'shtein S.
AU - Baier M.
PY - 2011
SP - 426
EP - 432
DO - 10.5220/0003362104260432