Fingerprint enhancement based on wavelet and anisotropic filtering

The importance of high-fidelity enhancement in low quality fingerprint image cannot be overemphasized. Most of the existing fingerprint enhancement methods are contextual filter-based methods and they often suffer from two shortcomings: (1) there is block effect on the enhanced images; and (2) they blur or destroy ridge structures around singular points. In order to well preserve the ridge structures in singular regions and avoid block effect, we develop a new method for fingerprint enhancement combining nontensor product wavelet filter banks and anisotropic filter. We first decompose the fingerprint image using the nontensor product wavelet filter banks. Then we modify the approximation subimage using anisotropic filtering and adjust the high frequency coefficients of the three other subimages by applying the adaptive approach to reduce the noises according to the geometry feature of images. Finally, the inverse transform is applied to map the result and a final contrast enhancement is done subsequently. Experiments have been conducted on the fingerprint database FVC2004 in our study. The results demonstrate that the proposed approach is capable of overcoming block effect and enhancing low quality fingerprint while preserving the ridge structures around singular points.