基于神经网络的潜在指纹分类

Hamid Jan, Amjad Ali
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引用次数: 2

摘要

该方法采用Gabor滤波、Haar和Daubechies小波变换以及多层神经网络对潜在指纹图像进行分类。进行了数值实验,并对所提解的结果进行了比较。结果表明,将Gabor滤波、Daubechies五阶小波变换和神经网络相结合,可以有效地对潜在指纹进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Latent Fingerprints Using Neural Networks
To classify latent fingerprint images by using papillary patterns, the proposed method applies the Gabor filter, Haar and Daubechies wavelet transformations, and a multilevel neural network. Numerical experiments were performed, and the results of the proposed solution were compared. The results have shown that we can effectively classify latent fingerprints by applying in combination the Gabor filter, Daubechies wavelet transform of the fifth level, and a neural network.
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