tailieunhanh - Báo cáo hóa học: "Research Article A Novel Criterion for Writer Enrolment Based on a Time-Normalized Signature Sample Entropy Measure"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Novel Criterion for Writer Enrolment Based on a Time-Normalized Signature Sample Entropy Measure | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 964746 12 pages doi 2009 964746 Research Article A Novel Criterion for Writer Enrolment Based on a Time-Normalized Signature Sample Entropy Measure Sonia Garcia-Salicetti Nesma Houmani and Bernadette Dorizzi Department of EPH Institut TELECOM TELECOM Management SudParis 91011 Evry France Correspondence should be addressed to Nesma Houmani Received 15 October 2008 Revised 8 March 2009 Accepted 9 June 2009 Recommended by Natalia A. Schmid This paper proposes a novel criterion for an improved writer enrolment based on an entropy measure for online genuine signatures. As online signature is a temporal signal we measure the time-normalized entropy of each genuine signature namely its average entropy per second. Entropy is computed locally on portions of a genuine signature based on local density estimation by a Client-Hidden Markov Model. The average time-normalized entropy computed on a set of genuine signatures allows then categorizing writers in an unsupervised way using a K-Means algorithm. Linearly separable and visually coherent classes of writers are obtained on MCYT-100 database and on a subset of BioSecure DS2 containing 104 persons DS2-104 . These categories can be analyzed in terms of variability and complexity measures that we have defined in this work. Moreover as each category can be associated with a signature prototype inherited from the K-Means procedure we can generalize the writer categorization process on the large subset DS2-382 from the same DS2 database containing 382 persons. Performance assessment shows that one category of signatures is significantly more reliable in the recognition phase and given the fact that our categorization can be used online we propose a novel criterion for enhanced writer enrolment. Copyright 2009 Sonia Garcia-Salicetti et al. This is an open access article distributed under the .