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.
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References
Maio D, Maltoni D, Cappelli R, Wayman J L, Jain A K. FVC2000: fingerprint verification competition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(3): 402–412
Hong L, Wan Y F, Jain A K. Fingerprint image enhancement: algorithm and performance evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(8): 777–789
Bazen A M, Gerez S H. Segmentation of fingerprint images. In: Proceedings of ProRISC 2001 Workshop on Circuits, Systems and Signal Processing. 2001, 276–280
Chen X J, Tian J, Cheng J G, Yang X. Segmentation of fingerprint images using linear classifier. EURASIP Journal on Applied Signal Processing, 2004, 2004(4): 480–494
Zhan X S, Sun Z C, Yin Y L, Chen Y. A method based on the Markov chain Monte Carlo for fingerprint image segmentation. In: Proceedings of 2nd International Conference on Fuzzy Systems and Knowledge Discovery. 2005, 240–248
Houlding D, Vaisey J. Low entropy image pyramids for efficient lossless coding. IEEE Transactions on Image Processing, 1995, 4(8): 1150–1153
Wallace K D, Marsh J N, Baldwin S L, Connolly A M, Keeling R, Lanza G M, Wickline S A, Hughes M S. Sensitive ultrasonic delineation of steroid treatment in living dystrophic mice with energy-based and entropy-based radio frequency signal processing. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2007, 54(11): 2291–2299
Battle D J, Harrison R P, Hedley M. Maximum entropy image reconstruction from sparsely sampled coherent field data. IEEE Transactions on Image Processing, 1997, 6(8): 1139–1147
Shen L L, Kot A C, Koo W M. Quality measures of fingerprint images. In: Proceedings of 3rd International Conference on Audioand Video-Based Biometric Person. 2001, 266–271
Freund Y, Schapire R E. A decision-theoretic generalization of online learning and an application to boosting. Journal of Computer and System Sciences, 1997, 55(1): 119–139
Duda R O, Hart P E, Stork D G. Pattern Classification. 2nd ed. New York: John Wily & Sons, Inc, 2000
Freund Y, Schapire R E. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 1999, 14(5): 771–780
Zheng X L, Wang Y S, Zhao X Y. Fingerprint image segmentation using active contour model. In: Proceedings of 4th International Conference on Image and Graphics. 2007, 437–441
Clark A F, Clark C. Performance characterization in computer vision: a tutorial. http://citeseerx.ist.psu.edu/viewdoc/download?
Maio D, Maltoni D, Cappelli R, Wayman J L, Jain A K. FVC2002: second fingerprint verification competition. In: Proceedings of 16th International Conference on Pattern Recognition. 2002, 811–814
Maio D, Maltoni D, Cappelli R, Wayman J L, Jain A K. FVC2004: Third fingerprint verification competition. In: Proceedings of 1st International Conference on Biometric Authentication. 2004, 1–7
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Liu, E., Zhao, H., Guo, F. et al. Fingerprint segmentation based on an AdaBoost classifier. Front. Comput. Sci. China 5, 148–157 (2011). https://doi.org/10.1007/s11704-011-9134-x
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DOI: https://doi.org/10.1007/s11704-011-9134-x