Fingerprint segmentation based on an AdaBoost classifier

Authors:
Eryun Liu;Heng Zhao;Fangfei Guo;Jimin Liang;Jie Tian
Affiliations:
Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China 710071;Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China 710071;Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China 710071;Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China 710071;Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China 710071 and Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190
Venue:
Frontiers of Computer Science in China
Year:
2011

Citing 11
Cited 0

A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Fingerprint Image Enhancement: Algorithm and Performance Evaluation

IEEE Transactions on Pattern Analysis and Machine Intelligence
FVC2000: Fingerprint Verification Competition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Quality Measures of Fingerprint Images

AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
FVC2002: Second Fingerprint Verification Competition

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Segmentation of fingerprint images using linear classifier

EURASIP Journal on Applied Signal Processing
Fingerprint Image Segmentation Using Active Contour Model

ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
A method based on the markov chain monte carlo for fingerprint image segmentation

FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Maximum entropy image reconstruction from sparsely sampled coherent field data

IEEE Transactions on Image Processing
Low entropy image pyramids for efficient lossless coding

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

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.