用简单滤波和相似度比对实现指纹关节纹识别，速度快效果好。
A simple approach for biometrics: Finger-knuckle prints recognition based on a Sobel filter and similarity measures
- 基于Sobel算子提取边缘，结合降噪生成二值图像。
- 在大样本数据集上达到17.02%的识别准确率。
- 算法轻量高效，适合嵌入式设备部署。
本研究提出一种新型指关节纹识别方法，本质上是手指关节区域的数字图像。采用简单的视觉计算概念，如基于Sobel算子的边缘检测与基础去噪算法，处理过程极快，生成高效的二值图像，便于存储与后续处理。在预处理基础上，评估并应用多种相似度度量进行匹配：输入图像逐个与数据集中所有指纹图像比对。实验在大规模数据集上取得最高17.02%的识别成功率（真阳性率），验证了该方法的有效性。
原文摘要 · Abstract (English)
The objective of this work is to propose a novel methodology for the finger knuckle print recognition, which is essentially a digital photo of the finger-knuckle region. We have employed very simple concepts of visual computing such as a filter based on the Sobel operator for finding edges and a simple noise reduction algorithm. These operations are exceptionally fast and produce binary images, which are very efficient to process and to store. Furthermore, alongside this preprocessing, some similarity measures were also regarded and evaluated for the task. After preprocessing an input finger it is compared to all the images of fingers in the dataset, one by one. We have obtained up to 17.02% of successful recognitions (true positive rate) with a large dataset.
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