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Proceedings Paper

Method based on quadric surface model for fingerprint image segmentation
Author(s): Yilong Yin; Xiukun Yang; Xv Chen; Haiyang Wang
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Paper Abstract

It is important to segment fingerprint image from background accurately, which could reduce time consumed on image preprocessing and improve the reliability of minutiae extraction. Methods for fingerprint image segmentation can be divided into two categories: at block level and at pixel level. This paper presents a method based on quadric surface model for fingerprint image segmentation, which belongs to method at pixel level. First, spatial distribution model of pixels based on Coherence, Mean and Variance is acquired and analyzed. 200 typical fingerprint images are selected from FVC2000 and FVC2002. The class of the pixel of these images, namely, fingerprint part or background part, is recorded manually. Coherence, Mean and Variance of each pixel are extracted and spatial distribution model of pixels is built by the use of different colors in displaying pixels of fingerprint part and pixels of background part. The model indicates that it is not linear apart and the performance of fingerprint image segmentation with a linear classifier is very limited. Second, a quadric surface formula is presented for fingerprint image segmentation and coefficients of the quadric surface formula are acquired by BP neural network. Last, in order to evaluate the performance of our method in comparison to a method using linear classifier, experiments are performed on FVC2000 DB2. Manual inspection shows that the proposed method provides accurate high-resolution segmentation results. Experimental result shows that only 0.97% of the pixels are misclassified by our method, and linear classifier misclassifies 6.8% of the pixels.

Paper Details

Date Published: 15 September 2004
PDF: 8 pages
Proc. SPIE 5403, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense III, (15 September 2004); doi: 10.1117/12.532612
Show Author Affiliations
Yilong Yin, Shandong Univ. (China)
Xiukun Yang, Identix Inc. (United States)
Xv Chen, Shandong Univ. (China)
Haiyang Wang, Shandong Univ. (China)


Published in SPIE Proceedings Vol. 5403:
Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense III
Edward M. Carapezza, Editor(s)

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