K-Means Based Fingerprint Segmentation with Sensor Interoperability. (6th May 2010)

• Record Type: Journal Article
• Authors: Yang, Gongping; Zhou, Guang-Tong; Yin, Yilong; Yang, Xiukun
• Other Names: Philips Wilfried R. Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2011(2011)
• Publication Date: 2010-05-06
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/729378
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : 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 ak -means based segmentation method called SKI. SKI clusters foreground and background blocks of a fingerprint image based on thek -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.