Towards optimal distortion-based visual privacy filters

The widespread usage of digital video surveillance systems has increased the concerns for privacy violation. Since video surveillance systems are invasive, it is a challenge to find an acceptable balance between privacy of the public under surveillance and security related features of the systems. Many privacy protection tools have been proposed for preserving privacy, ranging from such simple methods like blurring or pixelization to more advanced like scrambling and geometrical transform based filters. However, for a given filter implemented in a practical video surveillance system, it is necessary to know the strength with which the filter should be applied to protect privacy reliably. Assuming an automated surveillance system, this paper objectively investigates several privacy protection filters with varying strength degrees and determines their optimal strength values to achieve privacy protection. To this end, five privacy filters were applied to images from FERET dataset and the performance of three recognition algorithms was evaluated. The results show that different privacy protection filters influence the accuracy of different versions of face recognition differently and this influence depends both on the robustness of the recognition and the type of distortion filter