基于Gabor变换的指纹图像奇异点检测

Chih-Jen Lee, I. Jeng, Tai-Ning Yang, Chun-Jung Chen, Keng-Li Lin
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引用次数: 3

摘要

在指纹中,奇点(包括核心点和三角洲点)是非常重要的特征。对指纹类型进行分类,主要根据指纹核和指纹三角洲的数量和位置进行分类。对于指纹匹配,一些方法利用核心点对两幅指纹图像进行对齐,克服了旋转和平移的问题。因此,奇异点检测是指纹匹配和指纹分类的关键环节。奇异点检测过程必须具有鲁棒性;否则将严重影响整个指纹识别系统的性能。在本文中,我们将使用Gabor变换,通过一组完整的Gabor基函数进行采样,来证明奇异点区域的现象。此外,我们还将开发一种鲁棒的奇异点检测方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Singular points detection in fingerprint images using Gabor transform
In fingerprints, singular points, including cores and deltas, are very important features. To classify the types of fingerprints, the number and the position of cores and deltas are concerned. For fingerprint matching, some approaches used the core point to align two fingerprint images to overcome the problems from rotation and translation. Therefore, singular points detection is a critical process for both fingerprint matching and fingerprint classification. The process of singular points detection must be robust; otherwise, the performance of the whole fingerprint recognition system would be influenced heavily. In this paper, we will use Gabor transform, sampling by a complete set of Gabor basis functions, to demonstrate the phenomenon of the regions of singular points. Besides, we will develop a robust method to detect singular points.
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