A Machine Learning Approach to Off-Line Signature Verification Using Bayesian Inference
IWCF '09 Proceedings of the 3rd International Workshop on Computational Forensics
Analysis of Authentic Signatures and Forgeries
IWCF '09 Proceedings of the 3rd International Workshop on Computational Forensics
Visual-based online signature verification using features extracted from video
Journal of Network and Computer Applications
Synthetic on-line signature generation. Part I: Methodology and algorithms
Pattern Recognition
A study on the consistency and significance of local features in off-line signature verification
Pattern Recognition Letters
An online signature verification system for forgery and disguise detection
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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Recent results of forgery detection by implementing biometric signature verification methods are promising. At present, forensic signature verification in daily casework is performed through visual examination by trained forensic handwriting experts, without reliance on computer-assisted methods. With this competition on on- and offline skilled forgery detection, our objective is to make a first step towards bridging the gap between automated biometric performances and expert-based visual comparisons. We intent to combine realistic forensic casework with automated methods by testing systems on a forensic-like new dataset. The results achieved by the participating systems are promising: 2.85\% Equal Error Rate (EER) on the online data and 9.15\% on the offline data. From these results we indicate that automated methods might be able to support forensic handwriting experts (FHEs) to formulate the strength of evidence that needs to be reported in court in the future.