Time Series Based for Online Signature Verification

Pande Sutawan, Darma Putra

Abstract


Signature verification system is to match the tested signature with a claimed signature. This paper propose time series based for feature extraction method and dynamic time warping for match method. The system made by process of testing 900 signatures belong to 50 participants, 3 signatures for reference and 5 signatures from original user, simple imposters and trained imposters for signatures test. The final result system was tested with 50 participants with 3 references. This test obtained that system accuracy without imposters is 90,45 % at threshold 44 with rejection errors (FNMR) is 5,2% and acceptance errors (FMR) is 4,35 %, when with imposters system accuracy is 80,13 % at threshold 27 with error rejection (FNMR) is 15,6% and acceptance errors (average FMR) is 4,26 %, with details as follows: acceptance errors is 0,39%, acceptance errors simple imposters is 3,2% and acceptance errors trained imposters is 9,2%.


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References


Riha Z, Vaclav M. Biometric Authentication System. FI MU Report Series. 2000.

Plamondon R, Srihari N. On-line and Off-line handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis Machine Intelligence. 2000; 22(1): 63-84.

Anil J, Freidereke D G, Connell S D. On-line Signature Verification. Department of Computer Science and Engineering. Michigan State University.

Christian G. Signature Verification with Dynamic RBF Network and Time Series Motifs. University of Passau Germany.

Fangjun L, Shiliang M, Kaidong C, Xianfeng X. On-Line Handwritten Signature Verification Algorithm Based On Time Sequence. Institute for Scientific Computing and Information. 2005.

Gruber C, Gruber T, Sick B. Online Signature Verification with New Time Series Kernels for Support Vector Machines. Proceeding of ICB. Hongkong. 2006; 3832: 500–508.

Milton H, Fernando O. Handwritten Signature Authentication using Artificial Neural Networks.

Uthansakul, Peerapong, Uthansakul. Online Signature Verification Using Angular Transformation for e-Commerce Services. Word Academy of Science, Engineering and Technology, 2010.

Jayadevan R, Satish R K, Pradeep M P. Dynamic Time Warping Based Static Hand Printed Signature Verification. Journal of Pattern Recognition Research, 2009; pp. 52-65.

Hansheng L, Srinivas P, Venu G. Mouse Based Signature Verification for Secure Internet Transactions. State University of New York USA.

Kai H, Hong Y. On-Line Signature Verification Based on Stroke Matching. Electrical and Information Engineering University of Sydney Australia. 2006.

Alan M, Trevathan J, Read W, “Neural Network-based Handwritten Signature Verification”, School of Mathematics, Physics and Information Technology, James Cook University Australia, 2008.

Payman M and Amirhassan M S. Dynamic Online Signatures Recognition System Using a Novel Signature-Based Normalized Features String and MLP Neural Network. 2007.

Mailah M, Boon L. Biometric Signature Verification Using Pen Position, Time, Velocity and Pressure Parameters. University Technology. Malaysia. 2008.

Pranav P, Keogh E, Lin J, Lonardi S. Mining Motifs in Massive Time Series Databases. Proceeding of the ICDM. 2002; 2: 370–377.

Li W, Eamonn K. Semi-Supervised Time Series Classification. University of California.

Pratikakis Z K, Comelis I J, Nyssen E. Using Landmarks to Establish a Point-to-Point Correspondence between Signatures. Vrije University Brussel Belgium. 2000.

Zhifeng Y, Futai Z, Wenjie Y. Cryptanalysis to a Certificateless Threshold Signature Scheme. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2012; 10(6): 1496-1502.

Lizhen M. More Efficient VLR Group Signature Based on DTDH Assumption. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2012; 10(6): 1470-1476.

Dongqing Z, Yubing H, Xueyu T. Nonlinier/Non-Gaussian Time Series Prediction Based on RBF-HMM-GMM Model. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2012; 10(6): 1214-1226.




DOI: http://doi.org/10.12928/telkomnika.v11i4.1186

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