Fingerprint segmentation based on an AdaBoost classifier

doi:10.1007/s11704-011-9134-x

Front Comput Sci Chin

0, Vol.

Issue () : 148-157 https://doi.org/10.1007/s11704-011-9134-x

RESEARCH ARTICLE

Fingerprint segmentation based on an AdaBoost classifier

Eryun LIU¹, Heng ZHAO¹, Fangfei GUO¹, Jimin LIANG¹(

), Jie TIAN^1,2

1. Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi’an 710071, China; 2. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

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Abstract

Fingerprint segmentation is one of the most important preprocessing steps in an automatic fingerprint identification system (AFIS). Accurate segmentation of a fingerprint will greatly reduce the computation time of the following processing steps, and the most importantly, exclude many spurious minutiae located at the boundary of foreground. In this paper, a new fingerprint segmentation algorithm is presented. First, two new features, block entropy and block gradient entropy, are proposed. Then, an AdaBoost classifier is designed to discriminate between foreground and background blocks based on these two features and five other commonly used features. The classification error rate (Err) and McNemar’s test are used to evaluate the performance of our method. Experimental results on FVC2000, FVC2002 and FVC2004 show that our method outperforms other methods proposed in the literature both in accuracy and stability.

Keywords fingerprint segmentation entropy gradient entropy AdaBoost classifier McNemar’s test

Corresponding Author(s): LIANG Jimin,Email:jimleung@mail.xidian.edu.cn

Issue Date: 05 June 2011

Cite this article:

Eryun LIU,Heng ZHAO,Fangfei GUO, et al. Fingerprint segmentation based on an AdaBoost classifier[J]. Front Comput Sci Chin, 0, (): 148-157.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-011-9134-x
https://academic.hep.com.cn/fcs/EN/Y0/V/I/148

Fig.1 Sample images of FVC2000 database

Fig.2 Entropy and Gradient-entropy feature image of fingerprint. (a) Original image; (b) entropy image; (c) gradient-entropy image

Fig.3 Two examples of post processing. (a), (d) before post processing; (b), (e) the results after morphology operation; (c), (f) the results of contour smoothing

Tab.1 Parameters of the FVC databases [,,]

Fig.4 Segmentation results by (a) entropy feature; (b) gradient-entropy feature; (c) coherence feature; (d) mean feature; (e) variance feature; (f) Gabor mean feature; (g) Gabor standard deviation feature; (h) majority voting; (i) AdaBoost classifier

Fig.5 (s) of FVC2000 for (a) DB1; (b) DB2; (c) DB3; (d) DB4

Fig.6 (s) of FVC2002 for (a) DB1; (b) DB2; (c) DB3; (d) DB4

Fig.7 (s) of FVC2004 for (a) DB1; (b) DB2; (c) DB3; (d) DB4

Tab.2 Test results for two algorithms ( and )

Tab.3 Comparison of proposed algorithm with [] and [] on FVC2000 databases

Tab.4 Comparison of proposed algorithm with [] and [] on FVC2002 databases

Tab.5 Comparison of proposed algorithm with [] and [] on FVC2004 databases

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