Towards Gesture-Based User Authentication

Abstract

Video cameras are extensively used in modern surveillance systems to detect, track, and recognize, objects, people, and anomalies. Their use in user authentication, however, has been limited primarily to close-range face recognition systems. In this paper, we explore user authentication based on gestures captured by a video camera. Unlike pure biometrics, such as fingerprints, iris scans, and faces, gesture-based authentication combines irrevocable biometric information, such as the shapes and relative sizes of body parts, with voluntary movements which can be revoked. Our authentication method applies the empirical feature covariance matrix framework that has previously been used for tracking, face localization, and action recognition, to features extracted from body silhouettes. We have tested the performance of our algorithm in both user classification and user authentication on a database of 20 individuals performing 8 different gestures. We have obtained a 93-99% Correct Classification Rate (CCR) for user classification and a 5-6% Equal Error Rate (EER) for user authentication on single gestures from this dataset. This is a very encouraging result suggesting that gesture-based user authentication may be feasible in scenarios with a limited number of users.