Natural Scene Statistics commonly used in non-reference image quality measures and a deep learning based quality assessment approach are proposed as biometric quality indicators for vasculature images. While NIQE and BRISQUE if trained on common images with usual distortions do not work well for assessing vasculature pattern samples' quality, their variants being trained on high and low quality vasculature sample data behave as expected from a biometric quality estimator in most cases (deviations from the overall trend occur for certain datasets or feature extraction methods). The proposed deep learning based quality metric is capable of assigning the correct quality class to the vaculature pattern samples in most cases, independent of finger or hand vein patterns being assessed. The experiments were conducted on a total of 13 publicly available finger and hand vein datasets and involve three distinct template representations (two of them especially designed for vascular biometrics). The proposed (trained) quality measures are compared to a several classical quality metrics, with their achieved results underlining their promising behaviour.

翻译：自然场景统计（Natural Scene Statistics）常用于非参考图像质量度量，同时提出一种基于深度学习的质量评估方法，作为血管图像生物特征质量指标。尽管在常见失真图像上训练的NIQE和BRISQUE对评估血管模式样本质量效果不佳，但其针对高、低质量血管样本数据训练的变体在大多数情况下能如期望的生物特征质量估计器一般表现（对于特定数据集或特征提取方法，会偏离整体趋势）。所提出的基于深度学习的质量度量在大多数情况下能够为血管模式样本分配正确的质量等级，且与评估的是手指静脉还是手部静脉模式无关。实验共涉及13个公开的手指和手部静脉数据集，并包含三种不同的模板表示方法（其中两种专为血管生物特征设计）。将所提出的（经过训练的）质量度量与若干经典质量指标进行比较，其结果凸显了其优越性能。