Addressing Privacy Concerns in Depth Sensors

Abstract

Image-based assistive solutions raise concerns about the privacy of the individuals being monitored. The issue involves the situation when such technology is used in medical institutions to protect patients’ health and support the personnel. These devices are installed in facilities and process images that include personal and behavioral data during the day. Other types of images than RGB are used to maintain privacy in this type of application, like depth images. Usage of depth cameras in the majority of publications is considered private protective. This paper discusses the issue of privacy in vision-based applications using depth modality. The factors affecting privacy in depth images are presented. The main problem that makes an image non-private is that the subjects’ faces allow identification. This paper compares the Face Recognition (FR) technique between RGB and depth images. In the experimental part, a state-of-the-art model for FR in depth images is developed, which is used to establish boundary conditions when a person is recognized. The performance of FR between these two modalities is compared on two existing datasets containing images in both versions, including the training process. The study aims to determine under which conditions depth cameras preserve privacy and how much privacy they reveal.