An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmenting Handwritten Signatures at Their Perceptually Important Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off-line Signature Verification Using HMM for Random, Simple and Skilled Forgeries
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A comparison of SVM and HMM classifiers in the off-line signature verification
Pattern Recognition Letters
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Biometric identification is an emerging technology that can solve the security problems in our networked society. A lot of work has been done in the field of automatic off-line signature verification. While a large portion of the work is focused on random forgery detection, more efforts are still needed to address the problem of skilled forgery detection. In this paper a novel method for signature verification is proposed. In this method, each pixel belonging to signature is studied and endpoints from the geometry of the signature are extracted. A polygonal closed shape is drawn by joining these endpoints. Various structural features from the shape including parameter, area, minimum enclosing rectangle, rectangularity measure, and circularity measure and form factors are computed. These features were combined to build a verification function, which is evaluated using statistical procedures.