Learning partitioned least squares filters for fingerprint enhancement

S. Ghosal, Raghavendra Udupa, Sharath Pankanti, N. Ratha
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引用次数: 6

Abstract

Fingerprint images contain varying amount of noise because of the limitations of the fingerprint acquisition process. It is often necessary to enhance such noisy fingerprint images so that the features extracted from them are reliable. We propose a novel approach to fingerprint enhancement where a set of filters are learned using the "learn-from-example" paradigm. An expert provides the ground truth information for ridges in a small set of representative fingerprint images. The space of local fingerprint patterns in a small neighborhood is partitioned into a set of expressive yet computationally simple classes. A filter is learnt for each partition by finding the optimal linear mapping (in least-square sense) from the input to the enhanced space. The proposed approach offers distinct performance and speed advantages for a wide variety of fingerprint images.
学习指纹增强的分割最小二乘滤波器
由于指纹采集过程的限制,指纹图像包含不同数量的噪声。通常需要对这些有噪声的指纹图像进行增强,以便从中提取出可靠的特征。我们提出了一种新的指纹增强方法,其中使用“从例子中学习”范式学习一组过滤器。专家为一小组具有代表性的指纹图像中的脊提供地面真值信息。将小邻域内的局部指纹模式空间划分为一组表达性强且计算简单的类。通过寻找从输入到增强空间的最优线性映射(在最小二乘意义上)来学习每个分区的过滤器。该方法对各种指纹图像具有明显的性能和速度优势。
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