An Efficient Offline Handwritten Signature Verification Method Based on ODBTC Features

International Journal of Computer Trends and Technology (IJCTT)          
© 2018 by IJCTT Journal
Volume-61 Number-3
Year of Publication : 2018
Authors : A.Mary Jerin, S. Palanikumar, Resmi H B
DOI :  10.14445/22312803/IJCTT-V61P122


MLA Style: A.Mary Jerin, S. Palanikumar, Resmi H B "An Efficient Offline Handwritten Signature Verification Method Based on ODBTC Features" International Journal of Computer Trends and Technology 61.3 (2018):126-136.

APA Style:A.Mary Jerin, S. Palanikumar, Resmi H B (2018). An Efficient Offline Handwritten Signature Verification Method Based on ODBTC Features International Journal of Computer Trends and Technology, 61(3),126-136

The most widely recognized secure personal biometric authentication is handwritten signature. Most organizations, primarily concentrate on the visual appearance of the handwritten signature for confirmation purposes. Many archives, for ex- ample, forms, contracts, bank cheques, and credit card transactions require the handwritten signature. Main challenging issues in the system is features are used for recognizing forged and genuine signatures. This article presents a technique for offline hand- written signature verification by exploiting the advantage of low- complexity ordered-dither block truncation coding (ODBTC) for the generation of image content descriptor and Local binary patterns (LBP). LBP was widely used as a robust illumination invariant feature descriptor. The system consists of pre-processing, feature vector extraction, training and classification or verification stage. In the pre-processing stage ODBTC compresses a signature image into corresponding quantizers and bitmap image. The Bit Pattern Features (BPF) is generated from ODBTC encoded data streams and LBP feature is extracted from input image. In the training stage a set of reference, signature images for each person is used. The mean vector of the set of feature vector is used for the verification purpose. The relative distance measure was used for classification. The proposed system is executed and tested utilizing GPDS database. The performance of the system is measured and experimental result shows convenience and viability of the proposed system.

[1] Liu, Simon, and Mark Silverman. "A practical guide to biometric security technology." IT Professional 3.1 pp: 27-32, 2001
[2] Yilmaz, M.B., Yanikoglu, B., Tirkaz, C. and Kholmatov, A., October. Offline signature verification using classifier combination of HOG and LBP features. In Biometrics (IJCB), International Joint Conference on IEEE. (pp. 1-7). 2011
[3] Zheng, Rong, et al. "A framework for authorship identification of online messages: Writing?style features and classification techniques." Journal of the American society for information science and technology 57.3, pp: 378-393, 2006.
[4] Monrose, Fabian, and Aviel D. Rubin. "Keystroke dynamics as a biometric for authentication." Future Generation computer systems 16.4,pp: 351-359, 2000
[5] Iranmanesh, V., Ahmad, S.M.S., Adnan, W.A.W., Yussof, S., Arigbabu, O.A. and Malallah, F.L., “Online handwritten signature verification using neural network classifier based on principal component analysis”. The Scientific World Journal, 2014.
[6] López-García, M., Ramos-Lara, R., Miguel-Hurtado, O. and Cantó-Navarro, E., „„Embedded system for biometric online signature verification”. IEEE Transactions on industrial informatics, 10(1), pp.491-501. 2014.
[7] Sae-Bae, N. and Memon, N., “Online signature verification on mobile devices.” IEEE Transactions on Information Forensics and Security, 9(6), pp.933-947, 2014.
[8] Fayyaz, M., Saffar, M.H., Sabokrou, M., Hoseini, M. and Fathy, M., March. “Online signature verification based on feature representation”. In Artificial Intelligence and Signal Processing (AISP), 2015 International Symposium on IEEE.2663-2676.pp. 211-216. 2015.
[9] Xu, N., Guo, Y., Cheng, L., Wu, X. and Zhao, J., 2011, May. “A method for online signature verification based on neural network”. In Communication Software and Networks (ICCSN), IEEE 3rd International Conference on IEEE.pp. 357-360. 2011
[11] Shah, H., Pawar, P., Khachane, M.S., Sharma, S. and Pithava, S., “Online Signature Verification and Authentication using Smart Phones”.2016
[12] Lai, S., Jin, L. and Yang, W., “Online Signature Verification using Recurrent Neural Network and Length-normalized Path Signature”. arXiv preprint arXiv:1705.06849. 2017.
[13] Song, X., Xia, X. and Luan, F., “Online signature verification based on stable features extracted dynamically” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(10), pp.2663-2676. 2017.
[14] Guo, J.M. and Prasetyo, H., “Content-based image retrieval using features extracted from halftoning-based block truncation coding”. IEEE Transactions on image processing, 24(3), pp.1010-1024. 2015.
[15] Guo, J.M., “High efficiency ordered dither block truncation coding with dither array LUT and its scalable coding application”. Digital Signal Processing, 20(1), pp.97-110. 2010.
[16] Lin, J., Keogh, E., Wei, L. and Lonardi, S., “Experiencing SAX: a novel symbolic representation of time series”. Data Mining and knowledge discovery, 15(2), pp.107-144. 2007.
[17] Maini, R. and Aggarwal, H., “Study and comparison of various image edge detection techniques”. International journal of image processing (IJIP), 3(1), pp.1-11. 2009.
[18] Vargas, J.F., Ferrer, M.A., Travieso, C.M. and Alonso, J.B. “Off-line signature verification based on grey level information using texture features”. Pattern Recognition, 44(2), pp.375-385. 2011
[19] Pal, S., Alireza, A., Pal, U. and Blumenstein, M., December. “Multi-script off-line signature identification. In Hybrid Intelligent Systems (HIS)”, 2012 12th International Conference on . IEEE. pp. 236-240 2012.

Signature Verification, Matching processes, ODBTC features LBP, BPF, and relative distance.