OSRNet: Online Signature Recognition Network Utilising Spatio-Temporal Features Extracted from Signature Video

Signature being the most frequently used and widely accepted biometric has always attracted researchers due to complexities attached to its verification and analysis tasks. It can be categorized as offline or online based on the acquisition process. Online techniques are capable of capturing behavioral information. Signature possesses a unique spatial as well as temporal relation but suffers from large intra-class variation. Online signature verification techniques have been shown to yield better performance with respect to both intra-class variation as well as inter-class variation (forgery). This paper presents an online signature verification method where we propose: (i) a novel representation of the online signature data to video frames while maintaining structural and temporal information. (ii) a novel framework of spatio-temporal analysis for generated video frames to detect signature forgery. In this approach a method to extract long range temporal dependency is introduced which gives enhanced temporal context for better signature verification. We have conducted a series of experiments to train and validate our approach on the publicly available online signature datasets. Our proposed method outperforms the state-of-the-art, in online signature verification techniques and achieves an Equal Error Rate (EER) of 1.65% for skilled forgeries and 0.76% for random forgeries.