Face identity masking algorithms developed in recent years aim to protect the privacy of people in video recordings. These algorithms are designed to interfere with identification, while preserving information about facial actions. An important challenge is to preserve subtle actions in the eye region, while obscuring the salient identity cues from the eyes. We evaluated the effectiveness of identity-masking algorithms based on Canny filters, applied with and without eye enhancement, for interfering with identification and preserving facial actions. In Experiments 1 and 2, we tested human participants' ability to match the facial identity of a driver in a low resolution video to a high resolution facial image. Results showed that both masking methods impaired identification, and that eye enhancement did not alter the effectiveness of the Canny filter mask. In Experiment 3, we tested action preservation and found that neither method interfered significantly with driver action perception. We conclude that relatively simple, filter-based masking algorithms, which are suitable for application to low quality video, can be used in privacy protection without compromising action perception.
翻译:近些年来开发的面具身份保护算法旨在保护视频记录中人们的隐私。这些算法旨在干扰识别,同时保存关于面部动作的信息。一个重要的挑战是保存眼部区域的微妙动作,同时从眼睛中掩盖突出的身份提示。我们评估了基于Canny过滤器的伪造身份算法的有效性,该算法既应用又不增强眼睛,以干扰识别和保护面部动作。在实验1和2中,我们测试了人类参与者将低分辨率视频中驱动者的面部身份与高分辨率面部图像相匹配的能力。结果显示,两种遮盖方法都损害了识别功能,而眼睛增强并没有改变Canny过滤面具的效力。在实验3中,我们测试了行动保全,发现没有一种方法对驱动器动作的认知产生显著干扰。我们的结论是,相对简单、基于过滤的遮蔽算法,适合用于低质量视频应用,可以在不减损行动感知觉的情况下用于隐私保护。