A new software-based liveness detection approach using a novel fingerprint parameterization based on quality related features is proposed. The system is tested on a highly challenging database comprising over 10,500 real and fake images acquired with five sensors of different technologies and covering a wide range of direct attack scenarios in terms of materials and procedures followed to generate the gummy fingers. The proposed solution proves to be robust to the multi-scenario dataset, and presents an overall rate of 90% correctly classified samples. Furthermore, the liveness detection method presented has the added advantage over previously studied techniques of needing just one image from a finger to decide whether it is real or fake. This last characteristic provides the method with very valuable features as it makes it less intrusive, more user friendly, faster and reduces its implementation costs.

翻译：提出了一个基于软件的新型活性检测方法,采用基于质量相关特征的新型指纹参数,该系统在高度具有挑战性的数据库中测试,数据库由10 500多张真实和假图像组成,这些图像由五种不同技术传感器制成,涵盖各种直接攻击情景,其材料和程序为生成软手指所遵循。拟议解决方案证明对多设想数据集是健全的,提供了90%的准确分类样本。此外,所提出的活性检测方法比以前研究过的只需要一根手指图像来确定其是否真实或假的技巧具有更大的优势。最后一种特征提供了非常有价值的方法,因为它降低了侵扰性、更方便用户、更快和降低其执行成本。