Methods for person identification on a pressure-sensitive floor: Experiments with multiple classifiers and reject option

Abstract

This paper presents methods for footstep-based person identification using a large pressure-sensitive floor with a sensory system. The aim was to analyse and compare different pattern classification methods for their ability to solve this particular problem as well as to introduce some novel and useful methodological extensions, which can improve classification accuracy and the adaptability of the system. These extensions are based on the conditional posterior probability outputs of classifiers, i.e., efforts to combine classifiers trained with different feature sets and to combine multiple footstep instances of a single person walking on the floor. Additionally, a method to reject unreliable examples in order to increase accuracy was applied to the system. The experiments demonstrated the usefulness of these methods. An identification method that uses a combination of multiple classifiers and multiple examples yielded very promising results with an overall accuracy rate of 92% for ten different walkers. When the reject option was added, a classification rate of 95% with a 9% rejection rate was achieved. This methodology can be applied to smart room applications where a small number of persons need to be identified.