Bi-Directional Face Tracking for Precise Facial De-identification in Video Data

With video data being prevalently used in numerous applications, there is a substantial privacy risk due to the potential exposure of personal information embedded in these video datasets, especially facial features. In this work, we propose a method for concealing personal facial features within video content. It aims to safeguard individuals’ personal privacy by preventing inadvertent exposure and ensuring their anonymity. This method performs video content analysis, including scene detection, face detection, and tracking, and safeguards the personal privacy of individuals by applying face masking techniques, except for target individuals of interest. We have demonstrated the superior performance of our proposed method, as evidenced by the reduction in facial exposure scores. This indicates the method’s ability to consistently uphold privacy across video sequences while minimizing the unintended exposure of personal information.