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 ...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 segmenta- tion 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.展开更多
基金Acknowledgements This paper was supported by the National High Technology Research and Development Program of China （2008AA01Z411）, the National Natural Science Foundation of China （Grant Nos. 60902083, 60803151, and 60875018）, the Beijing Natural Science Fund （4091004）, and the Fundamental Research Funds for the Central Universities （K50510100003）.
文摘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 segmenta- tion 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.
