Deep Learning Methods for Signature Verification
This addresses identity verification for security applications, but appears incremental as it builds on existing methods.
The paper tackled signature verification by proposing deep learning architectures and improving Path Signature Features with temporal encoding, demonstrating effectiveness in experiments.
Signature is widely used in human daily lives, and serves as a supplementary characteristic for verifying human identity. However, there is rare work of verifying signature. In this paper, we propose a few deep learning architectures to tackle this task, ranging from CNN, RNN to CNN-RNN compact model. We also improve Path Signature Features by encoding temporal information in order to enlarge the discrepancy between genuine and forgery signatures. Our numerical experiments demonstrate the effectiveness of our constructed models and features representations.