@inproceedings{1c54b12407b7463f8439b6a9c25ec24a,
title = "Learning partitioned least squares filters for fingerprint enhancement",
abstract = "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.",
keywords = "Band pass filters, Computational efficiency, Feature extraction, Fingerprint recognition, Frequency estimation, Gabor filters, Image matching, Least squares methods, Matched filters, Nonlinear filters",
author = "S. Ghosal and R. Udupa and S. Panknti and Ratha, \{N. K.\}",
note = "Publisher Copyright: {\textcopyright} 2000 IEEE.; 5th IEEE Workshop on Applications of Computer Vision, WACV 2000 ; Conference date: 04-12-2000 Through 06-12-2000",
year = "2000",
doi = "10.1109/WACV.2000.895395",
language = "English",
series = "Proceedings of IEEE Workshop on Applications of Computer Vision",
publisher = "IEEE Computer Society",
pages = "2--7",
booktitle = "Proceedings - 5th IEEE Workshop on Applications of Computer Vision, WACV 2000",
address = "United States",
}