Integrating Face-ID into an Interactive Person-ID Learning System

Abstract

Acquiring knowledge about persons is a key functionality for humanoid robots. By envisioning a robot that can provide personalized services the system needs to detect, recognize and memorize information about specific persons. To reach this goal we present an approach for extensible person identification based on visual processing, as one component of an interactive system able to interactively acquire information about persons. This paper describes an approach for face-ID recognition and identification over image sequences and its integration into the interactive system. We compare the approach of sequence hypotheses against results from single image hypotheses, and a standard approach and show improvements in both cases. We furthermore explore the usage of confidence scores to allow other system components to estimate the accuracy of face-ID hypotheses.