Two Deep Learning Solutions for Automatic Blurring of Faces in Videos

TL;DR

This paper introduces two deep learning methods for automatic face blurring in videos, aiming to enhance privacy protection in surveillance footage through object detection and segmentation techniques.

Contribution

It presents two novel deep learning solutions—one using YOLO for face detection and blurring, and another employing a Unet-like segmentation network for face anonymization.

Findings

Both methods effectively detect and blur faces in videos.

The segmentation approach provides more precise face masking.

The object detection method offers faster processing times.

Abstract

The widespread use of cameras in everyday life situations generates a vast amount of data that may contain sensitive information about the people and vehicles moving in front of them (location, license plates, physical characteristics, etc). In particular, people's faces are recorded by surveillance cameras in public spaces. In order to ensure the privacy of individuals, face blurring techniques can be applied to the collected videos. In this paper we present two deep-learning based options to tackle the problem. First, a direct approach, consisting of a classical object detector (based on the YOLO architecture) trained to detect faces, which are subsequently blurred. Second, an indirect approach, in which a Unet-like segmentation network is trained to output a version of the input image in which all the faces have been blurred.