Leap signature recognition using HOOF and HOT features

With the growing need for secure authentication, there is an increasing interest in establishing newer biometric modalities that are verifiable in a fast manner with as few associated complexities as possible. In this research, we propose a new biometric modality using a Leap Motion device. The Leap signature is created by an individual in three-dimensional space in absence of any feedback from objects or surfaces. The proposed framework combines an adaptation of 3D Histogram of Oriented Optical Flow and a new feature descriptor, termed as Histogram of Oriented Trajectories. Experiments are performed on the IIITD Leap Signature Database, which consists of 900 samples from 60 subjects. The results are combined with a four-patch local binary pattern based face verification algorithm. An accuracy of over 91% is achieved on this database, with rate of successful spoofing attempts being approximately 1.4%.