In order to improve the quality of fingerprint with a large noise, this study proposes a fingerprint enhancement method by using a sparse representation of learned multi-scale classification dictionaries with reduced dimensionality. The multi-scale dictionary is used to balance the contradiction between the accuracy and the anti-noise ability, which is an ideal solution to reconcile the demands of enhancement quality and computational performance. The principal component analysis is applied in the authors’ technique for dimension reduction of multi-scale classification dictionaries. Under the quality grading scheme and multi-scale composite windows, the fingerprint patches are enhanced by using a sparse representation of learned multi-scale classification dictionaries with reduced dimensionality according to their priorities. In addition, the multi-scale composite windows help the more high-quality spectra diffuse into the low-quality fingerprint patches and this can greatly improve the spectra quality of them. Experimental results and comparisons on FVC 2000 and FVC 2004 databases are reported. And it shows that the proposed method yields better results in terms of the robustness of fingerprint enhancement as compared with the latest techniques. Moreover, the results show that the proposed algorithm can obtain better identification performance.

中文翻译：

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使用降维的多尺度分类词典增强指纹

为了提高大噪声下指纹的质量，本研究提出了一种使用稀疏表示的学习的多尺度分类词典的降维方法来增强指纹的方法。多尺度字典用于平衡精度和抗噪能力之间的矛盾，这是协调增强质量和计算性能要求的理想解决方案。主成分分析应用于作者的技术中，用于多尺度分类词典的降维。在质量分级方案和多尺度复合窗口下，通过使用稀疏表示的学习的多尺度分类词典的稀疏表示来增强指纹斑块，这些字典根据其优先级降低维数。此外，多尺度的复合窗口有助于将更多高质量的光谱扩散到低质量的指纹图块中，从而可以大大提高其光谱质量。报告了FVC 2000和FVC 2004数据库的实验结果和比较结果。并且表明，与最新技术相比，该方法在指纹增强的鲁棒性方面产生了更好的结果。实验结果表明，该算法具有较好的识别性能。并且表明，与最新技术相比，该方法在指纹增强的鲁棒性方面产生了更好的结果。实验结果表明，该算法具有较好的识别性能。并且表明，与最新技术相比，该方法在指纹增强的鲁棒性方面产生了更好的结果。实验结果表明，该算法具有较好的识别性能。