1 research outputs found
Evaluating the Effectiveness of Automated Identity Masking (AIM) Methods with Human Perception and a Deep Convolutional Neural Network (CNN)
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 studied the effectiveness of eight
de-identification algorithms using human perceivers and a state-of-the-art deep
convolutional neural network (CNN). We used a standard face recognition
experiment in which human subjects studied high-resolution (studio-style)
images to learn driver identities. Subjects were tested subsequently on their
ability to recognize those identities in low-resolution videos depicting the
drivers operating a motor vehicle. The videos were in either unmasked format,
or were masked by one of the eight de-identification algorithms. All masking
algorithms lowered identification accuracy substantially, relative to the
unmasked video. In all cases, identifications were made with stringent decision
criteria indicating the subjects had low confidence in their decisions. When
matching the identities in high-resolution still images to those in the masked
videos, the CNN performed at chance. Next, we examined CNN performance on the
same task, but using the unmasked videos and their masked counterparts. In this
case, the network scored surprisingly well on a subset of mask conditions. 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. We note that no approach is equally effective in masking
all stimuli, and that future work should examine possible methods for
determining the most effective mask per individual stimulus.Comment: *K.O.H and A.B. contributed equally to this work.10 pages, 4 tables,
7 figure