Authors:
A. Pillai
;
S. Mil'shtein
and
M. Baier
Affiliation:
University of Massachusetts, United States
Keyword(s):
Fingerprints recognition, Binarizing, Alignment, Fourier transform.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
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.
(More)