Artificial Intelligence and Computer Vision Laboratory, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China.
Sensors (Basel). 2021 Mar 8;21(5):1885. doi: 10.3390/s21051885.
Finger vein (FV) biometrics is one of the most promising individual recognition traits, which has the capabilities of uniqueness, anti-forgery, and bio-assay, etc. However, due to the restricts of imaging environments, the acquired FV images are easily degraded to low-contrast, blur, as well as serious noise disturbance. Therefore, how to extract more efficient and robust features from these low-quality FV images, remains to be addressed. In this paper, a novel feature extraction method of FV images is presented, which combines curvature and radon-like features (RLF). First, an enhanced vein pattern image is obtained by calculating the mean curvature of each pixel in the original FV image. Then, a specific implementation of RLF is developed and performed on the previously obtained vein pattern image, which can effectively aggregate the dispersed spatial information around the vein structures, thus highlight vein patterns and suppress spurious non-boundary responses and noises. Finally, a smoother vein structure image is obtained for subsequent matching and verification. Compared with the existing curvature-based recognition methods, the proposed method can not only preserve the inherent vein patterns, but also eliminate most of the pseudo vein information, so as to restore more smoothing and genuine vein structure information. In order to assess the performance of our proposed RLF-based method, we conducted comprehensive experiments on three public FV databases and a self-built FV database (which contains 37,080 samples that derived from 1030 individuals). The experimental results denoted that RLF-based feature extraction method can obtain more complete and continuous vein patterns, as well as better recognition accuracy.
手指静脉（FV）生物识别技术是最有前途的个体识别特征之一，具有独特性、防伪造和生物测定等能力。然而，由于成像环境的限制，获取的 FV 图像容易出现对比度低、模糊以及严重的噪声干扰等问题。因此，如何从这些低质量的 FV 图像中提取更高效、更鲁棒的特征仍然是一个待解决的问题。本文提出了一种新的 FV 图像特征提取方法，该方法结合了曲率和类 Radon 特征（RLF）。首先，通过计算原始 FV 图像中每个像素的平均曲率来获得增强的静脉模式图像。然后，对先前获得的静脉模式图像进行特定的 RLF 实现，这可以有效地聚集静脉结构周围分散的空间信息，从而突出静脉模式并抑制虚假的非边界响应和噪声。最后，获得更平滑的静脉结构图像，以便进行后续的匹配和验证。与现有的基于曲率的识别方法相比，所提出的方法不仅可以保留固有的静脉模式，还可以消除大部分伪静脉信息，从而恢复更平滑和真实的静脉结构信息。为了评估我们提出的基于 RLF 的方法的性能，我们在三个公共的 FV 数据库和一个自建的 FV 数据库（包含 37080 个样本，来源于 1030 个人）上进行了全面的实验。实验结果表明，基于 RLF 的特征提取方法可以获得更完整和连续的静脉模式，以及更好的识别精度。
