Abstract
The identification of low-resolution fingerprints has always been one of the focuses in the field of biometric identification. This paper proposes a method for super-resolving low-resolution fingerprints based on deep dictionary learning. First, it is necessary to obtain a priori based on the fingerprint ridge orientation. After obtaining it, the ridges of the fingerprint are divided into n categories according to the direction. Each class uses deep dictionary learning models to train corresponding high- and low-resolution dictionaries respectively. In the super-resolution part, after extracting features from the patches that require super-resolution, the sparse coefficients are obtained through the deep dictionary learning model, and then combined with the high-resolution dictionary to obtain high-resolution patches, which are combined into high-resolution fingerprints. Experimental results show that the proposed method performs better than some other methods.
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Acknowledgements
The work is partially supported by Natural Science Foundation of Anhui Provincial (Grant No. 2108085MF206) and National Natural Science Foundation of China (Grant No. 61976006).
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Bian contributed to the conception of the study, revised the manuscript and provided founding support; Huang contributed significantly to analysis and wrote the manuscript; Xu performed the data analyses and manuscript preparation; Jie helped perform the analysis with constructive discussions and provided founding support; Luo performed the experiments. All authors reviewed the manuscript.
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Huang, Y., Bian, W., Xu, D. et al. Fingerprint image super-resolution based on multi-class deep dictionary learning and ridge prior. SIViP 18, 5491–5501 (2024). https://doi.org/10.1007/s11760-024-03249-3
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DOI: https://doi.org/10.1007/s11760-024-03249-3