Full Body Person Identification Using the Kinect Sensor

Abstract

Identifying individuals using biometric data is an important task in surveillance, authentication and even entertainment. This task is more challenging when required to be performed without physical contact and at a distance. Analyzing video footages from individuals for patterns is an active area of research aiming at fulfilling this goal. We describe results on classifiers trained to identify individuals from data collected from 140 subjects walking in front of a Microsoft Kinect sensor, which allows tracking 3D points representing a subject's skeleton. From this data we extract anthropometric and gait attributes to be used by the classifiers. We show that anthropometric features are more important than gait features but using both allows for higher accuracies. Additionally, we explore how different numbers of subjects and numbers of available examples affect accuracy, providing evidences on how effective the proposed methodology can be in different scenarios.