An Improved K-Means Based Method for Fingerprint Segmentation with Sensor Interoperability

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Fingerprint segmentation is an important step in an automatic fingerprint recognition system. Due to applications of various sensors, fingerprint segmentation inevitably suffers from sensor interoperability problem. K-means algorithm is one solution to address the sensor interoperability problem in fingerprint segmentation. However, the traditional k-means based method does not well deal with the border between the foreground and the background. The over-segmentation of foreground area may appear and some important minutiae are lost. To effectively address the issue, we propose an improved k-means based segmentation method with sensor interoperability called ISKI. ISKI performs the secondary determination to the blocks which have similar distances with the two cluster centers after k-means clustering. The proposed method is applied on a number of fingerprint databases which are collected by various sensors. Experimental results show our proposed method significantly improves the accuracy of segmentation.

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Periodical:

Edited by:

Robin G. Qiu and Yongfeng Ju

Pages:

237-243

DOI:

10.4028/www.scientific.net/AMM.135-136.237

Citation:

Z. G. Yang et al., "An Improved K-Means Based Method for Fingerprint Segmentation with Sensor Interoperability", Applied Mechanics and Materials, Vols. 135-136, pp. 237-243, 2012

Online since:

October 2011

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$35.00

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