Leveraging shape and depth in user authentication from in-air hand gestures

Depth-sensors, such as the Kinect, have predominately been used as a gesture recognition device. Recent works, however, have proposed using these sensors for user authentication using biometric modalities such as: face, speech, gait and gesture. The last of these modalities - gestures, used in the context of full-body and hand-based gestures, is relatively new but has shown promising authentication performance. In this paper, we focus on hand-based gestures that are performed in-air. We present a novel approach to user authentication from such gestures by leveraging a temporal hierarchy of depth-aware silhouette covariances. Further, we investigate the usefulness of shape and depth information in this modality, as well as the importance of hand movement when performing a gesture. By exploiting both shape and depth information our method attains an average 1.92% Equal Error Rate (EER) on a dataset of 21 users across 4 predefined hand-gestures. Our method consistently outperforms related methods on this dataset.