Abstract
Although fingerprint recognition has been widely studied for personnel recognition in the past decades, it is still challenging problem to achieve reliable feature extraction and recognition for poor quality fingerprints. Fingerprint enhancement is often incorporated prior to feature extraction to improve the quality of fingerprint and achieve higher recognition accuracy. Motivated by the recent success of sparse representation in image denoising, this paper proposes a fingerprint enhancement method by using sparse representation. First, a set of Gabor basis functions with various orientations and frequencies are used to build a redundant dictionary for fingerprint representation. Then, the fingerprint enhancement problem is modeled as an iterative sparse representation of the local patch fingerprint, which can be solved by L1-norm regularized minimization. Experimental results and comparison on FVC fingerprint databases are presented to show the effectiveness of the proposed method on fingerprint enhancement, especially for poor quality fingerprints.
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References
Bazen, A.M., Gerez, S.H.: Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(7), 905–919 (2002)
Cands, E.J., Tao, T.: Near optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans. on Information Theory 52(12), 5406–5425 (2006)
Cappelli, R., Maio, D., Maltoni, D.: Semi-automatic enhancement of very low quality fingerprints. In: Proceedings of 6th International Symposium on Image and Signal Processing and Analysis, Salzburg (2009)
Chikkerur, S., Cartwright, A.N., Govindaraju, V.: Fingerprint enhancement using stft analysis. Pattern Recognition 40(1), 198–211 (2007)
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Processing 15(12), 3736–3745 (2006)
Garris, M.D., Watson, C.I., McCabe, R.M., Wilson, C.L.: User’s guide to nist fingerprint image software. National Institute of Science and Technology (2001)
Hong, L., Wan, Y., Jain, A.K.: Fingerprint image enhancement: Algorithm and performance evaluation. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(8), 777–789 (1998)
Lee, H., Battle, A., Raina, R., Ng, A.: Efficient sparse coding algorithms. In: Advances in Neural Information Processing Systems (NIPS), pp. 801–808 (2007)
Maio, D., Davide Maltoni, R., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC 2004: Third fingerprint verification competition. In: Proceedings of International Conference on Biometric Authentication, Hong Kong, pp. 1–7 (July 2004)
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, A.: Handbook of Fingerprint Recognition. Springer, New York (2003)
Zhao, Q., Zhang, L., Zhang, D., Huang, W., Bai, J.: Curvature and singularity driven diffusion for oriented pattern enhancement with singular points. In: CVPR, Florida, USA, pp. 2129–2135 (2009)
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Wang, X., Liu, M. (2013). Fingerprint Enhancement via Sparse Representation. In: Sun, Z., Shan, S., Yang, G., Zhou, J., Wang, Y., Yin, Y. (eds) Biometric Recognition. CCBR 2013. Lecture Notes in Computer Science, vol 8232. Springer, Cham. https://doi.org/10.1007/978-3-319-02961-0_24
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DOI: https://doi.org/10.1007/978-3-319-02961-0_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02960-3
Online ISBN: 978-3-319-02961-0
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