A Novel Spatio-Temporal Siamese Network for 3D Signature Recognition

Abstract Signature forgery is at the centre of several fraudulent activities and legal battles. The introduction of 3D signatures, the virtual signing of ones name in the air, has the potential to restrict forgers due to the absence of visual cues that can be easily copied. Existing 3D signature recognition approaches, however, have not leveraged the inherent spatial and temporal information, making it difficult to handle the diminished separability and reproducibility of these signatures. In this paper, we propose a novel spatio-temporal adaptation of the Siamese Neural Network, wherein one branch extracts spatial features using a 1D Convolutional Neural Network (CNN) while the other processes the input in the temporal domain using Long Short-Term Memory networks (LSTMs). Unlike conventional deep learning networks, Siamese networks are an application of One-Shot Learning so as to learn from a small amount of data as is often the case in real life problems. They employ a distance metric that is forced to be small for like samples (signatures from the same person), and large for different samples (from different persons). The proposed approach, termed ST-SNN, is compared to other baseline classification architectures, and demonstrated using a publicly available biometric 3D signature benchmark dataset, yielding True Positive Rate (TPR) of 94.63% with 4.1% False Acceptance Rate (FAR).