For a robot to perform personalised human support in a home, it is essential to uniquely identify the people it is working for. Each individual has their own attributes, knowledge of the environment, and experiences which the robot should learn and keep in its memory. One of the current challenges is for a robot to identify and re-identify an individual while dealing with visual occlusion, changes in appearance, and identity switching. This paper presents a novel approach to address these challenges in robot vision by extending current state-of-the-art solutions to enhance person memorisation by taking multiple viewpoints to extract features for matching and maintaining this information in a world model of the robot.