K-Means Based Fingerprint Segmentation with Sensor Interoperability
A critical step in an automatic fingerprint recognition system is the segmentation of fingerprint images. Existing methods are usually designed to segment fingerprint images originated from a certain sensor. Thus their performances are significantly affected when dealing with fingerprints collected...
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2010-01-01
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Series: | EURASIP Journal on Advances in Signal Processing |
Online Access: | http://dx.doi.org/10.1155/2010/729378 |
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doaj-6660f0f44fa241c3bd3380028609912a2020-11-24T23:55:16ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802010-01-01201010.1155/2010/729378K-Means Based Fingerprint Segmentation with Sensor InteroperabilityXiukun YangYilong YinGuang-Tong ZhouGongping YangA critical step in an automatic fingerprint recognition system is the segmentation of fingerprint images. Existing methods are usually designed to segment fingerprint images originated from a certain sensor. Thus their performances are significantly affected when dealing with fingerprints collected by different sensors. This work studies the sensor interoperability of fingerprint segmentation algorithms, which refers to the algorithm's ability to adapt to the raw fingerprints obtained from different sensors. We empirically analyze the sensor interoperability problem, and effectively address the issue by proposing a k-means based segmentation method called SKI. SKI clusters foreground and background blocks of a fingerprint image based on the k-means algorithm, where a fingerprint block is represented by a 3-dimensional feature vector consisting of block-wise coherence, mean, and variance (abbreviated as CMV). SKI also employs morphological postprocessing to achieve favorable segmentation results. We perform SKI on each fingerprint to ensure sensor interoperability. The interoperability and robustness of our method are validated by experiments performed on a number of fingerprint databases which are obtained from various sensors. http://dx.doi.org/10.1155/2010/729378 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiukun Yang Yilong Yin Guang-Tong Zhou Gongping Yang |
spellingShingle |
Xiukun Yang Yilong Yin Guang-Tong Zhou Gongping Yang K-Means Based Fingerprint Segmentation with Sensor Interoperability EURASIP Journal on Advances in Signal Processing |
author_facet |
Xiukun Yang Yilong Yin Guang-Tong Zhou Gongping Yang |
author_sort |
Xiukun Yang |
title |
K-Means Based Fingerprint Segmentation with Sensor Interoperability |
title_short |
K-Means Based Fingerprint Segmentation with Sensor Interoperability |
title_full |
K-Means Based Fingerprint Segmentation with Sensor Interoperability |
title_fullStr |
K-Means Based Fingerprint Segmentation with Sensor Interoperability |
title_full_unstemmed |
K-Means Based Fingerprint Segmentation with Sensor Interoperability |
title_sort |
k-means based fingerprint segmentation with sensor interoperability |
publisher |
SpringerOpen |
series |
EURASIP Journal on Advances in Signal Processing |
issn |
1687-6172 1687-6180 |
publishDate |
2010-01-01 |
description |
A critical step in an automatic fingerprint recognition system is the segmentation of fingerprint images. Existing methods are usually designed to segment fingerprint images originated from a certain sensor. Thus their performances are significantly affected when dealing with fingerprints collected by different sensors. This work studies the sensor interoperability of fingerprint segmentation algorithms, which refers to the algorithm's ability to adapt to the raw fingerprints obtained from different sensors. We empirically analyze the sensor interoperability problem, and effectively address the issue by proposing a k-means based segmentation method called SKI. SKI clusters foreground and background blocks of a fingerprint image based on the k-means algorithm, where a fingerprint block is represented by a 3-dimensional feature vector consisting of block-wise coherence, mean, and variance (abbreviated as CMV). SKI also employs morphological postprocessing to achieve favorable segmentation results. We perform SKI on each fingerprint to ensure sensor interoperability. The interoperability and robustness of our method are validated by experiments performed on a number of fingerprint databases which are obtained from various sensors. |
url |
http://dx.doi.org/10.1155/2010/729378 |
work_keys_str_mv |
AT xiukunyang kmeansbasedfingerprintsegmentationwithsensorinteroperability AT yilongyin kmeansbasedfingerprintsegmentationwithsensorinteroperability AT guangtongzhou kmeansbasedfingerprintsegmentationwithsensorinteroperability AT gongpingyang kmeansbasedfingerprintsegmentationwithsensorinteroperability |
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1725463228106932224 |