Fingerprint Enhancement Algorithm Based-on Gradient Magnitude for the Estimation of Orientation Fields
An accurate estimation of fingerprint orientation fields is an important step in the fingerprint classification process. Gradient-based approaches are often used for estimating orientation fields of ridge structures but this method is susceptible to noise. Enhancement of fingerprint images improves the ridge-valley structure and increases the number of correct features thereby conducing the overall performance of the classification process. In this paper, we propose an algorithm to improve ridge orientation textures using gradient magnitude. That algorithm has four steps; firstly, normalization of fingerprint image, secondly, foreground extraction, thirdly, noise areas identification and marking using gradient coherence and finally, enhancement of grey level. We have used standard fingerprint database NIST-DB14 for testing of proposed algorithm to verify the degree of efficiency of algorithm. The experiment results suggest that our enhanced algorithm achieves visibly better noise resistance with other methods.