Oriented diffusion filtering for enhancing low-quality fingerprint images

To enhance low-quality fingerprint images, we present a novel method that first estimates the local orientation of the fingerprint ridge and valley flow and next performs oriented diffusion filtering, followed by a locally adaptive contrast enhancement step. By applying the authors' new approach to low-quality images of the FVC2004 fingerprint databases, the authors are able to show its competitiveness with other state-of-the-art enhancement methods for fingerprints like curved Gabor filtering. A major advantage of oriented diffusion filtering over those is its computational efficiency. Combining oriented diffusion filtering with curved Gabor filters led to additional improvements and, to the best of the authors' knowledge, the lowest equal error rates achieved so far using MINDTCT and BOZORTH3 on the FVC2004 databases. The recognition performance and the computational efficiency of the method suggest to include oriented diffusion filtering as a standard image enhancement add-on module for real-time fingerprint recognition systems. In order to facilitate the reproduction of these results, an implementation of the oriented diffusion filtering for Matlab and GNU Octave is made available for download.