HMM-based on-line signature verification: Feature extraction and signature modeling

This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters 28.16 (2007): 2325 – 2334, DOI: 10.1016/j.patrec.2007.07.012A function-based approach to on-line signature verification is presented. The system uses a set of time sequences and Hidden Markov Models (HMMs). Development and evaluation experiments are reported on a subcorpus of the MCYT bimodal biometric database comprising more than 7,000 signatures from 145 subjects. The system is compared to other state-of-the-art systems based on the results of the First International Signature Verification Competition (SVC 2004). A number of practical findings related to feature extraction and modeling are obtained.This work has been supported by the Spanish projects TIC2003-08382-C05- 01 and TEC2006-13141-C03-03, and by the European NoE Biosecure