Learning features for offline handwritten signature verification using spatial transformer network

Offline handwritten signatures are one of the most common and widely accepted symbols in the field of biometrics and document forensics, and they are frequently used for daily attendance checks, credit card payments, and business contracts to verify an individual's identity. However, offline signature verification remains a challenging task due to the difficulty in discriminating minute yet significant details between genuine and skillfully forged signatures. To tackle this issue, this paper proposes a two-stage Siamese network model for offline handwritten signature verification using spatial transformer network. It is implemented with two-fold interesting ideas: (a) an efficient spatial transformation network module is introduced to reconstruct the spatial position of handwriting and automatically guide the model to focus on important features while ignoring redundant information, and (b) adopting the Focal loss function to overcome the extreme imbalance between positive and negative signature samples. Experimental results on four challenging handwritten signature datasets with different languages demonstrate that our proposed model outperforms state-of-the-art models in terms of verification accuracy.