论文标题
基于深脸表示的可靠检测Doppelgäng。
Reliable Detection of Doppelgängers based on Deep Face Representations
论文作者
论文摘要
Doppelgängers（或LookAlikes）通常会在面部识别系统中提高错误匹配的可能性，而不是选择用于非合法比较试验的随机面部图像对。在这项工作中，我们使用最先进的面部识别系统评估了Doppelgängers对野生数据库中HDADoppelgänger和变相面孔的影响。发现Doppelgänger图像对产生非常高的相似性评分，从而显着提高了错误的匹配率。此外，我们提出了一种doppelgänger检测方法，该方法通过分析从面部图像对获得的深度表示的差异，从而区分了doppelgängäng。所提出的检测系统采用基于机器学习的分类器，该分类器通过面部变形技术对生成的Doppelgänger图像对训练。在HDADoppelgänger和类似的面部数据库上进行的实验评估揭示了检测出的误差率约为2.7％，即与Doppelgängers分离配对的身份验证尝试的任务约为2.7％。
Doppelgängers (or lookalikes) usually yield an increased probability of false matches in a facial recognition system, as opposed to random face image pairs selected for non-mated comparison trials. In this work, we assess the impact of doppelgängers on the HDA Doppelgänger and Disguised Faces in The Wild databases using a state-of-the-art face recognition system. It is found that doppelgänger image pairs yield very high similarity scores resulting in a significant increase of false match rates. Further, we propose a doppelgänger detection method which distinguishes doppelgängers from mated comparison trials by analysing differences in deep representations obtained from face image pairs. The proposed detection system employs a machine learning-based classifier, which is trained with generated doppelgänger image pairs utilising face morphing techniques. Experimental evaluations conducted on the HDA Doppelgänger and Look-Alike Face databases reveal a detection equal error rate of approximately 2.7% for the task of separating mated authentication attempts from doppelgängers.
