SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification (1707.02131v2)

Abstract: Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction.

• The paper presents SigNet, a convolutional Siamese network that automates feature learning for robust, writer-independent offline signature verification.
• The study demonstrates state-of-the-art performance across datasets like CEDAR, GPDS300, GPDS Synthetic, and BHSig260, achieving perfect accuracy on CEDAR.
• The paper highlights practical applications in banking and document authentication, setting the stage for future enhancements in biometric systems.

Critical Evaluation of "SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification"

The paper "SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification" presents a methodologically robust approach to the challenging task of offline signature verification using a convolutional Siamese network. The paper addresses the problem of verifying signatures without overfitting to specific handwriting styles, a critical requirement for scalable, writer-independent biometric verification systems.

Technical Details and Contributions

The central contribution of this paper is the development of a convolutional Siamese network, termed SigNet, tailored for offline signature verification. Unlike traditional methods that rely on hand-crafted features, SigNet employs deep learning to automatically learn features from signature images, which enables it to handle discrepancies between genuine and forged signatures. The network employs twin convolutional subnetworks with shared weights, optimizing a feature space where genuine signatures are closely mapped together, while forged samples are distantly mapped.

The rigorous experimental assessment presented in the paper underscores SigNet's efficacy across multiple datasets, including CEDAR, GPDS300, GPDS Synthetic, and BHSig260. The paper highlights that SigNet achieves superior accuracy on several benchmark datasets. Notably, it delivers a perfect accuracy on the CEDAR dataset, which is particularly commendable given the complexity and variability of handwritten signatures. The network also shows competitive performance on the GPDS Synthetic dataset, showcasing its generalizability and robustness against diverse signature styles and forgery techniques.

Implications and Future Developments

The implications of this research extend both practically and theoretically. From a practical standpoint, SigNet's ability to effectively differentiate between genuine and forged signatures without customized training for each user is a significant advancement for real-world applications. Systems such as banking verification and document authentication services can leverage this writer-independent feature learning to maintain high accuracy with minimal retraining demands.

On a theoretical level, this paper reinforces the potential of convolutional Siamese architectures in handling verification tasks involving significant intra-class variability. It provides a baseline for future exploration of deep learning methods tailored for biometric verification, especially in scenarios lacking abundant data for each individual subject.

Looking ahead, the paper suggests promising directions for future research. Enhancements to the SigNet architecture could integrate more sophisticated feature extraction layers or attention mechanisms to further improve signature differentiation. Additionally, evolving network structures for multi-modal biometric systems could incorporate SigNet as a component to bolster security and accuracy further.

Conclusion

The paper offers a comprehensive exploration of using Siamese networks for offline signature verification, marking a notable contribution to the domain of biometric authentication. By eschewing hand-crafted features in favor of learned representations, SigNet sets a new precedent for accuracy and efficiency in signature verification systems. The results achieved on cross-domain datasets not only validate the proposed approach but also open new avenues for implementing robust and adaptable biometric systems across various domains.