International Journal of
Computer Sciences and Engineering

Scholarly, Peer-Reviewed and Fully Refereed Academic Research Journal

Flash News 

Full paper submission has now been opened for August edition. You can upload your full paper using the required templates to the Online Submission System. Deadline for uploading the full papers is 22 August 2018.

Enhanced Approach on Online Handwritten Signature Verification through Multi rate SVM with Wavelet Transformation
Open Access   Article

Enhanced Approach on Online Handwritten Signature Verification through Multi rate SVM with Wavelet Transformation
Prathibha MK1 , Basavaraj L2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-7 , Page no. 58-65, Jul-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i7.5865

Online published on Jul 31, 2018

Copyright © Prathibha MK, Basavaraj L . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 
View this paper at   Google Scholar | DPI Digital Library
  XML View PDF Download  
Citation

IEEE Style Citation: Prathibha MK, Basavaraj L, “Enhanced Approach on Online Handwritten Signature Verification through Multi rate SVM with Wavelet Transformation”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.58-65, 2018.

MLA Style Citation: Prathibha MK, Basavaraj L "Enhanced Approach on Online Handwritten Signature Verification through Multi rate SVM with Wavelet Transformation." International Journal of Computer Sciences and Engineering 6.7 (2018): 58-65.

APA Style Citation: Prathibha MK, Basavaraj L, (2018). Enhanced Approach on Online Handwritten Signature Verification through Multi rate SVM with Wavelet Transformation. International Journal of Computer Sciences and Engineering, 6(7), 58-65.
VIEWS PDF XML
36 55 downloads 6 downloads
  
  
           
Abstract :
Online Handwritten Signature verification plays a significant role in the field of administrative, banking, business sector, etc. Therefore, an accurate signature verification system is required in order to provide an identification of an individual. A new Online Handwritten Signature verification is proposed based on a Multirate Support Vector Machine (MSVM) and for verification the SUSIG database is used. The input database is obtained from the pressure sensitive tablet, removal of noise and resizing is done through fourth order wavelet and discrete cosine transform. Further, the functional feature such as standard deviation, skewness etc. are extracted and processed to MSVM for generation of threshold value between genuine and sample signature. The obtained result is more sensitive, specific and accurate. The Equal Error rate (EER) of 0.33 is obtained, so that the proposed system shows competitive performance with the other existing approaches.
Key-Words / Index Term :
Online Handwritten Signature Verification,(OHSV), Multirate Support Vector Model (MSVM), Discrete, Wavelet Transform (DWT), Discrete Cosine Transformation (DCT), Feature Extraction, Forgery, Threshold value
References :
[1] Nitin Tiwari, "An Overview and Analysis Based on Biometric Framework Technique and Fingerprint Biometric Technology", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.69-74, 2017
[2] Abughfa, A.B. Elmadani, "Offline Signature Verification Based on Image Processing and Hu Moment", International Journal of Scientific Research in Network Security and Communication, Vol.4, Issue.5, pp.1-7, 2016.
[3] EdigaLingappa, Geetavani.B, JambulaHareesha , "Online Signature Verification using Dynamic Properties", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.33-38, 2017.
[4] Kashi R, Hu J, Nelson W L and Turin W “Hidden Markov Model Approach to On-Line Handwritten Signature Verification”, Conf. Document Analysis and Recognition 4: pp.253-257, 1997.
[5] Alister K and Yanikoglu B, identity Authentication Using Improved On-Line Signature Verification Method. Pattern Recognition Letters 26: pp. 2400-2408,2005.
[6] Fierrez, J, Garcia J O, Ramos D and Rodriguez J G “HMM based on-line signature verification: Feature extraction and signature modelling
‘,. Pattern Recognition Letters. 28: pp.2325–2334 2007.
[7] Kholmatov A and Yanikoglu B “Identity authentication using improved Online Handwritten Signature verification method”, Pattern Recognition. Letter 26: pp. 2400–2408, 2005.
[8] Kour J, Hanmandlu, M and Ansar, A Q “Online Handwritten Signature verification using GA-SVM”, International Conference on Image Information Processing (ICIIP 2011), pp. 1 – 4, 2011.
[9] Durrani M Y, Khan S and Khalid S, “VerSig: a new approach for Online Signature verification”, Cluster Computing, pp. 1-11, 2017
[10 Ibrahim M T, Kyan M and Guan L “On-line signature verification using global features”, Canadian conference on Electrical and Computer Engineering, (CCECE`09), pp. 682-685, 2009.
[11] Song X, Xia X. and Luan F “Online Handwritten Signature verification based on stable features extracted dynamically”, IEEE Transactions on Systems, Man and Cybernetics: Systems 47: pp.2663-2676, 2017.
[12] Guru D S and Prakash H N “Online Handwritten Signature verification and recognition: An approach based on symbolic representation”, IEEE transactions on pattern analysis and machine intelligence 31: pp.1059 – 1073, 2009.
[13] Jain A K, Griess F D and Connell S D “On-line signature verification”, Pattern Recognition 35: pp. 2963– 2972, 2002.
[14] Zalasiński M “New algorithm for on-line signature verification using characteristic global features”, In: Information Systems Architecture and Technology: Proceedings of 36th International Conference, pp. 137- 146, 2016.
[15] Khalil M I, Moustafa, M and Abbas H M “Enhanced DTW based on-line signature verification”, In: Conf. Image Processing (ICIP) 16: pp. 2713-2716,2009.
[16] Vaseghi B, and Hashemi, S “Online Handwritten Signature Verification Using Vector Quantization and Hidden Markov Model”, IOSR Journal of Electronics and Communication Engineering (IOSR- JECE)10: pp.48-53,2015.
[17] Draouhard J P, Sabourin R and Godbout M “A neural network approach to Off-line Signature verification using directional PDF”, Pattern Recognition 29: pp.415-424, 1996.
[18] Salvador and Chan P “Toward accurate dynamic time warping in linear time and space”, Intelligent Data Analysis 11: pp. 61-71,2007.
[19] Liu Y, Yang, Z, and Yang, L “Online Handwritten Signature verification based on DCT and sparse representation”, IEEE transactions on cybernetics 45: pp.2498-2511,2015.
[20] García M L, Lara R R, Hurtado O M, Navarro E C “Embedded system for biometric Online Handwritten Signature verification”, IEEE Transactions on industrial informatics 10: pp.491- 501,2014.
[21] Fischer A, Diaz M, Plamondon R.,and Ferrer M A “Robust score normalization for DTW-based on- line signature verification”. Int. conf. Document Analysis and Recognition (ICDAR), pp.241- 245,2015.
[22] Zalasiński M, Cpałka K and Andersson E R. “An idea of the dynamic signature verification based on a hybrid approach”, In: International Conference on Artificial Intelligence and Soft Computing, pp.232-246, 2016.
[23] Aguilar J F, Krawczyk S, Garcia J O and Jain A K, “Fusion of local and regional approaches for on- line signature verification”, In: Proc. Int. Conf. Adv. Biometric Recognition Syst. (IWBRS), pp. 188–196, 2005.
[24] Rehman, A U, Rehman S, Babar Z H, Qadeer M K and Seelro F A “Offline Signature Recognition and Verification System Using Artificial Neural Network”, University of Sindh Journal of Information and Communication Technology 2: pp.73-80, 2018.
[25] 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, pp.1-8, 2014.
[26] Diaz M, Fischer A, Ferrer M A, and Plamondon R. “Dynamic signature verification system based on one-real signature”, IEEE transactions on cybernetics 48: pp.228-239,2018.
[27] Manjunatha K S, Manjunath S, Guru D S, and Somashekara M T “Online Signature verification based on writer dependent features and classifiers”, Pattern Recognition Letters 80: pp.129-136, 2016.
[28] Baraki P and Ramaswamy V “Biometric Authentication of a Person Using Mouse Gesture Dynamics: An Innovative Approach”, In: International Proceedings on Advances in Soft Computing, Intelligent Systems and Application 7: pp. 331-344,2018.
[29] Rohilla S and Sharma A “SVM Based Online Signature Verification Technique Using Reference Feature Vector”, Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 87: pp.125-136,2017.
[30] Roy D, Chowdhury A, Sihnaray A and Ghose A “Novel Handwritten signature verification system based on shadow sensing”, In: IEEE SENSORS, pp. 1-3,2017.
[31] Serdouk Y, Nemmour H and Chibani Y “Handwritten signature verification using the quad-tree histogram of templates and a Support Vector-based artificial immune classification”, Image and Vision Computing 66: pp. 26-35,2017.
[32] Tran V Q, Chew M T, Demidenko S, Kuang Y C, and Ooi M “Simple signature verification sub- system for identity recognition”, In: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp. 94-98,2017.
[33] Kiran G V, Kunte R S R and Samuel S “On-line signature verification system using probabilistic feature modelling”, In: Sixth International Symposium on Signal Processing and its Applications, pp.355- 358,2001.
[34] Khalid M, Mokayed H, Yuso, R, and Ono O 2009 Online Handwritten Signature verification with neural networks classifier and fuzzy inference. In:Third Asia International Conference Modelling & Simulation (AMS`09), pp. 236-241.
[35] Reza A G, Lim H and Alam M J “An Efficient Online Handwritten Signature Verification Scheme Using Dynamic Programming of String Matching”, In: International Conference on Hybrid Information Technology 6935: pp. 590–597,2011.
[36] Gruber C, Gruber T, Krinninger S and Sick B “Online Handwritten Signature verification with support vector machines based on LCSS kernel function”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40: pp. 1088-1100,2010.