Recent developments in three-dimensional sensing devices has led to the proposal of a number of biometric modalities for non-critical scenarios. Leap Motion device has received attention from Vision and Biometrics community due to its high precision tracking. In this research, we propose Leap Password; a novel approach for biometric authentication. The Leap Password consists of a string of successive gestures performed by the user during which physiological as well as behavioral information is captured. The Conditional Mutual Information Maximization algorithm selects the optimal feature set from the extracted information. Match-score fusion is performed to reconcile information from multiple classifiers. Experiments are performed on the Leap Password Dataset, which consists of over 1700 samples obtained from 150 subjects. An accuracy of over 81% is achieved, which shows the effectiveness of the proposed approach. © 2015 IEEE.