Face de-identification algorithms have been developed in response to the prevalent use of public video recordings and surveillance cameras. Here, we evaluated the success of identity masking in the context of monitoring drivers as they actively operate a motor vehicle. We compared the effectiveness of eight de-identification algorithms using human perceivers. The algorithms we tested included the personalized supervised bilinear regression method for Facial Action Transfer (FAT), the DMask method, which renders a generic avatar face, and two edge-detection methods implemented with and without image polarity inversion (Canny, Scharr). We also used an Overmask approach that combined the FAT and Canny methods. We compared these identity masking methods to identification of an unmasked video of the driver. Human subjects were tested in a standard face recognition experiment in which they learned driver identities with a high resolution (studio-style) image, and were tested subsequently on their ability to recognize masked and unmasked videos of these individuals driving. All masking methods lowered identification accuracy substantially, relative to the unmasked video. The most successful methods, DMask and Canny, lowered human identification performance to near random. In all cases, identifications were made with stringent decision criteria indicating the subjects had low confidence in their decisions. We conclude that carefully tested de-identification approaches, used alone or in combination, can be an effective tool for protecting the privacy of individuals captured in videos. Future work should examine how the most effective methods fare in preserving facial action recognition.
翻译:针对普遍使用公共视频记录和监视摄像机的情况,我们开发了脸色解剖算法。在这里,我们评估了身份掩码在监测司机积极操作机动车辆方面是否成功。我们比较了8个使用人类感知器的解剖算法的有效性。我们测试的算法包括个人化监督双线回归法,用于法西行动转移(FAT),Dmask方法,该方法使这些驾驶者具有通用的阿凡达脸色,以及两种边缘探测法,既采用图像极化,又不采用图像极化(Canny, Scharr)。我们还使用了将FAT和Canny方法相结合的 " overmask " 方法。我们将这些身份掩码方法与识别司机的无包装视频方法进行了比较。在标准面色化实验中,他们学习了具有高分辨率( studio)图像的司机身份双线回归法,随后又测试了他们识别这些驾驶者蒙面和无面具的视频的能力。所有遮掩码方法都大大降低了识别的准确度,与未涂面视频的准确性。最成功的方法,我们将这些方法与最成功的方法进行了比较,Damask和Candegraphismismism 做了研究。在选择中,我们用了一个非常的模型研究。我们用了一种方法,在选择了一种方法,用一个非常的模型来判断方法来判断。