结合滤波和深度玻尔兹曼机重构的指纹增强

Combined Filtering and DBM Reconstructing for Fingerprint Enhancement

指纹图像增强,是自动指纹识别系统中的重要环节.为弥补传统指纹图像增强算法的缺陷,提出一种指纹图像增强算法.在指纹块质量分级机制和复合窗口策略下,指纹图像首先在频域被具有方向选择性的方向高斯带通滤波器滤波增强;随后,二值增强指纹中的误增强区域在空域被具有方向选择性的深度玻尔兹曼机(DBM)重构.提出的方法结合了传统指纹增强算法与深度学习算法的优点,拥有很强的容错能力,能够完成对低质量指纹图像的有效增强.为了验证提出算法的性能,在公开的指纹数据库 FVC2004 上进行了大量实验,实验结果表明,相比于传统的指纹增强算法,提出的方法具有很强的鲁棒性,对高质量和低质量指纹均有不俗的增强表现.

The enhancement of fingerprint plays an important role in automatic fingerprint identification system. In order to make up for the shortcomings of the traditional fingerprint enhancement, this study proposes a novel algorithm by using orientation Gaussian bandpass filter (OGBPF) to enhance the fingerprint firstly, and then the deep Boltzmann machine (DBM) with orientation selection is employed to reconstruct these regions that are enhanced incorrectly in the first phase. The fingerprint is enhanced based on the quality grading scheme and the composite window strategy. In the proposed method, the traditional enhancement method and deep learning method complement one another perfectly. To validate the performance, the proposed method has been applied to fingerprint enhancement on the FVC2004 databases. Experiments show that, compared with the state-of-the-art enhancement methods, the proposed method is more accurate and more robust against noise, and can achieve better results.