Learning partitioned least squares filters for fingerprint enhancement
Fingerprint images contain varying amount of noise because of the limitations of the fingerprint acquisition process. It is often necessary to enhance such noisy fingerprint images so that the features extracted from them are reliable. We propose a novel approach to fingerprint enhancement where a set of filters are learned using the "learn-from-example" paradigm. An expert provides the ground truth information for ridges in a small set of representative fingerprint images. The space of local fingerprint patterns in a small neighborhood is partitioned into a set of expressive yet computationally simple classes. A filter is learnt for each partition by finding the optimal linear mapping (in least-square sense) from the input to the enhanced space. The proposed approach offers distinct performance and speed advantages for a wide variety of fingerprint images.