Abstract
Fingerprint image enhancement is of key importance for the efficiency of the automated fingerprint identification system. Before we can enhance a fingerprint image with contextual filters, we need to enhance fingerprint image contrast and readability with non-directional filters. This article provides an analysis of the influence of non-directional image enhancement techniques in the fingerprint image pre-processing step. To perform the analysis we used global normalization algorithm, local normalization algorithm, Wiener filter, histogram equalization algorithm and median filter. To evaluate the equal error rate in the experiments, done on a public database FVC_2004, we used Gabor filter and a state-of-the-art two-stage algorithm. DOI: http://dx.doi.org/10.5755/j01.eee.20.6.7277
Highlights
In the spatial domain we can divide fingerprint image enhancement algorithms as follows: non-directional filtering, one-directional filtering and two-directional filtering
The second section provides a review of certain nondirectional pre-processing enhancement techniques, the third section describes fingerprint image enhancement with contextual Gabor filter, the fourth section describes fingerprint image enhancement with a two-stage algorithm, the fifth section presents experimental results, and the final section sums up the conclusions
With experimental results we found that Wiener and median filters are inefficient in combination with contextual filters, as most structures of ridges and valleys falls into the non-correctable area, which is eliminated from the image
Summary
In the spatial domain we can divide fingerprint image enhancement algorithms as follows: non-directional filtering, one-directional filtering and two-directional filtering Nondirectional filtering techniques, such as histogram equalization [1], normalization [2], Wiener filtering [3], and median filtering [1] are because of the non-stationary nature of the image efficient only in the pre-processing step and in particular enhance the image contrast and readability, but they do not change ridge structures which are deformed because of noise or other external factors. It is necessary to enhance the image to the point where we can obtain a clearly defined structure of ridges and valleys on the Manuscript received December 12, 2013; accepted February 9, 2014 This operation was partly financed by the European Union, European Social Fund. Contextual Gabor filter, the fourth section describes fingerprint image enhancement with a two-stage algorithm, the fifth section presents experimental results, and the final section sums up the conclusions
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