BLUR & TRACK: Real-time Face Detection with Immediate Blurring and Efficient Tracking
Tanakrit Jaichuen, Nanthaphop Ren, Pichai Wongapinya, Somchart Fugkeaw · 2023
This paper proposes the BLUR & TRACK system that anonymizes detected face based on blurring algorithm and supports efficient retrieval of the specified human face recorded in the video file. The development of our system has been driven by the privacy regulations such as GDPA and PDPA that enforce the data controllers and data processors to be aware of the high protection of personal data privacy. Most CCTVs available in the markets are not initially designed to serve the face blurring of people while the video files have been recorded. This is vulnerable to privacy breaches if those files are not strictly controlled with appropriate access control mechanisms. In this paper, our BLUR & TRACK system incorporates two major functions including blurring the human face while the video is running and the efficient tracking function that supports the face query by authorized person. To this end, we used the image frame to do face and object detection, extract the area of interest for the face and object based on region of interest (ROI). Then, we applied blurring to ROI by combining every frame that was blurred into the video. Then, they are kept in the graph database for efficient retrieval. Finally, we reported the experiment results related to the precision and recall of our proposed scheme when it was implemented with RetinaFace and YOLOv5Face models.