Beyond Privacy of Depth Sensors in Active and Assisted Living Devices

Abstract

Active and Assisted Leaving (AAL) devices that use cameras and images raise concerns about the privacy of the monitored individuals. These devices capture images that include personal and behavioral data during the day. Most authors decide to switch from RGB to depth sensors to maintain privacy. Nevertheless, not all available works agree that depth image is private, which creates an open legal problem for AAL applications. In this paper, privacy is discussed in vision-based systems using depth sensors. Various factors of depth and RGB images that might affect privacy are presented to define the privacy level of depth devices. One of the main issues that make an image non-private is that the subjects' faces are visible and can be identified. In the experimental part, a state-of-the-art Face Recognition (FR) model in depth images is developed. It is used to establish boundary conditions allowing correct recognition of a person's face. A comparison between FR in RGB and depth images is performed, including the ability to learn the model by training the two modalities from scratch on identical data. This study answers under which conditions depth cameras protect the privacy and how much privacy is disclosed by them.