Curved Gabor Filters for Fingerprint Image Enhancement

TL;DR

This paper introduces curved Gabor filters that adapt to local ridge orientations for improved fingerprint image enhancement, especially in noisy, low-quality images, outperforming existing methods.

Contribution

The paper presents a novel curved Gabor filter design that aligns with local ridge flow, enhancing low-quality fingerprint images more effectively than traditional filters.

Findings

Improved ridge clarity in noisy fingerprint images.

Enhanced fingerprint matching accuracy.

Outperforms state-of-the-art enhancement techniques.

Abstract

Gabor filters play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved Gabor filters which locally adapt their shape to the direction of flow. These curved Gabor filters enable the choice of filter parameters which increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved Gabor filters are applied to the curved ridge and valley structure of low-quality fingerprint images. First, we combine two orientation field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation and they are used for estimating the local ridge frequency. Lastly, curved Gabor filters are defined based on curved regions and they are applied for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison to state-of-the-art enhancement methods.