Toward Privacy-Protecting Safety Systems for Naturalistic Driving Videos

A common pool of naturalistic driving data is necessary to develop and compare algorithms that infer driver behavior, in order to improve driving safety. Naturalistic driving data, such as video sequences of looking at a driver, however, cause concern for the privacy of individual drivers. In an ideal situation, a deidentification filter applied to a raw image of looking at a driver would, semantically, protect the identity and preserve the behavior (e.g, eye gaze, head pose, and hand activity) of the driver. Driver gaze estimation is of particular interest because it is a good indicator of a driver´s visual attention and a good predictor of a driver´s intent. Interestingly, the same facial features that are explicitly or implicitly used for gaze estimation play a key role in recognizing a person´s identity. In this paper, we implement a specific deidentification filter on video sequences of looking at a driver from naturalistic driving and present novel findings on its effect on face recognition and driver gaze-zone estimation.