Authors:
D. Palma, P.L. Montessoro, G. Giordano, and F. Blanchini
Date:
2019
Publisher:
IEEE
Journal:
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Cite:
D. Palma, P.L. Montessoro, G. Giordano, and F. Blanchini, "Biometric palmprint verification: a dynamical system approach," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 12, pp. 2676-2687, 2019.
Bibtex:
@article{PMGB2019TSMC,
title = {Biometric Palmprint Verification: A Dynamical System Approach},
author = {Palma, David and Montessoro, Pier Luca and Giordano, Giulia and Blanchini, Franco},
journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},
volume = {49},
number = {12},
pages = {2676--2687},
year = {2019},
publisher = {IEEE}
}Abstract:
Most of the existing techniques for palmprint recognition rely on metrics, typically based on static functions, which evaluate the distance between a pair of features. In this paper, we propose a new technique for palmprint verification based on a dynamical system approach for principal palm lines matching. The proposed dynamic algorithm is recursive and involves a positive linear dynamical system, whose evolution depends on the matching level between the two input images. In a preprocessing phase, the procedure iteratively erodes both of the images to be compared, by eliminating points in each image that do not have enough close neighboring points both in the image itself and the comparison image. As a result of the iterations, only the points that have enough neighboring points in both the image itself and in the comparison image can survive. Thus, the output of the dynamical system converges either to zero, when a deep mismatch exists between the two images, or to a high value, when a good matching is observed. The results, in terms of verification, are in line with the state-of-the-art results in the current literature. The main advantage of the approach is its robustness when dealing with low-resolution and noisy images. The impact of noise (e.g., salt and pepper noise) is effectively reduced: images corrupted with such noise are easily recognized, while a randomly generated image is rejected even when compared with itself.