Privacy Protecting, Real-time Face Re-recognition

Abstract

We present a novel system for recognizing human individuals walking past a depth camera that is compatible with privacy protecting laws. The system is developed to support the statistical analysis of movement patterns in indoor spaces. The system is able to re-recognize previously seen individuals but is also capable of recognizing that an individual has not been seen before. The system is designed in a privacy protecting way and does not rely on previously collected training data but rather collects data during run-time. The proposed system processes each image of an individual separately, but we also present a new approach that is based on combining several decisions into a single meta-decision in order to enhance classification performance.