The Value of Multiple Viewpoints in Gesture-Based User Authentication

Although traditionally used as a gesture recognition device, the Kinect has been recently leveraged for user entry control. In this context, a user admission decision is typically based on biometrics such as face, speech, gait and gestures. Despite being a relatively new biometric, gestures have been shown to be a promising authentication modality. These results have been achieved using a single Kinect camera. This paper aims to investigate the potential performance and robustness gains in gesture-based user authentication using multiple Kinects. We study the impact of multiple viewpoints on a dataset of 40 users that contains notable degradations from user memory and personal effects (multiple types of bags and outerwear). We found that two additional viewpoints can provide as much as 26 -- 43% average relative improvement in the Equal Error Rate (EER) for user authentication, and as much as 16 -- 68% average relative improvement in the Correct Classification Error (CCE) compared to using a single centered Kinect camera.