Bayesian recognition of human identities from continuous visual features for safe and secure access in healthcare environments

When using today´s admission control systems some kind of interaction is generally required. That is because the system needs something to check a person´s identity before it allows access. Often the system will ask for a pin or a password. For many people these interactions are a problem as they might be unable interact with the system in the required way due to impairments. This can be caused by a disease such as Parkinson´s or Alzheimer´s or they might be unable to use their hands for other reasons. In hospitals, retirement homes or assisted living communities many people would benefit from a contactless admission control system. We propose a system that recognizes human identities from continuous geometrical features that were estimated using the Kinect 2. Those include the distance between the eyes and the estimated height of a person. A Bayesian Classifier for continuous features is used to predict people´s identities. The preliminary recognition results suggest that the proposed geometrical features can contribute to reliable admission control. This work was supported by SysTeam GmbH Dortmund, Germany.