论文标题
面部图像质量估计对演示攻击检测的影响
Impact of Face Image Quality Estimation on Presentation Attack Detection
论文作者
论文摘要
非指南的面部图像质量评估方法已成为面部识别系统的预过滤步骤。在其中大多数中，质量得分通常是考虑到面部匹配的设计。但是，在衡量其对演示攻击检测（PAD）的影响和实用性方面已经完成了少量工作。在本文中，我们研究了质量评估方法对过滤真正的攻击样本，对PAD系统的影响以及在经过过滤（按质量）数据集进行训练时如何提高此类系统的性能的影响。在视觉变压器垫算法上，通过去除较低质量的样本来减少20％的训练数据集，这使我们在跨数据库测试中可以将BPCer提高3％。
Non-referential face image quality assessment methods have gained popularity as a pre-filtering step on face recognition systems. In most of them, the quality score is usually designed with face matching in mind. However, a small amount of work has been done on measuring their impact and usefulness on Presentation Attack Detection (PAD). In this paper, we study the effect of quality assessment methods on filtering bona fide and attack samples, their impact on PAD systems, and how the performance of such systems is improved when training on a filtered (by quality) dataset. On a Vision Transformer PAD algorithm, a reduction of 20% of the training dataset by removing lower quality samples allowed us to improve the BPCER by 3% in a cross-dataset test.
