Development of a Sigma-Lognormal representation for on-line signatures
Pattern Recognition
A study on enhanced dynamic signature verification for the embedded system
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
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This paper presents a novel method of on-line signature verification that analyzes both the shape of the signature and the dynamic of the writing process. This approach automatically determines characteristic features of the writing image and combines these shape features with features from the writing dynamic. For establishing a writing characteristic template for one signer the signature is separated into characteristic segments. The segmentation algorithm extracts writing points which would give a forgery the appearance of the original. For these significant points local extreme values, which identify writing segments, are calculated. Subsequently, dynamic features are computed for the segments. The developed system needs three signatures of one person for the establishment of a personalized template. A database has been collected with 544 signatures of 27 signers for evaluation. The developed system achieved a right acceptance rate of 78% and a right rejection rate of 100%.