Abstract
Signatures are widely used for authentication purposes. Signature verification has many applications in banking, in crossing international borders, in boarding of planes etc. For verifying identity of a person, signature is legally and widely accepted biometric trait. This work presents two simple and efficient methods for online signature verification. The paper proposes Support Vector Machine (SVM) and Random Forest Method for verifying online signatures. To measure the performance of algorithm f1 score is used and experiments were performed on SUSIG dataset. In Method-1, data after preprocessing is taken as feature set and in Method-2 feature vector are made by concatenation of bins of different attributes of the signature. The attributes taken are kth derivative of x and y coordinates, and kth derivatives of pressure. The classification is performed on extracted features using SVM and Random Forest. The performances of proposed methods were evaluated by using confusion matrix on SUSIG dataset. Results show that the proposed methods are capable of verifying online signatures with acceptable level of accuracy.
Supported by Ministry of Education, Government of India.
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References
Kholmatov, A., Yanikoglu, B.: SUSIG: An on-line signature database, associated protocols and benchmark results. J. Pattern Anal. Appl. 12(3), 227–236 (2009)
Impedovo, D., Pirlo, G., Plamondon, R.: Handwritten signature verification: new advancements and open issues. In: 2012 International Conference on Frontiers in Handwriting Recognition, pp. 367–372, September 2012
Lei, H., Govindaraju, V.: A comparative study on the consistency of features in on-line signature verification. Pattern Recogn. Lett. 26(15), 2483–2489 (2005)
Dolfing, J.G.A., Aarts, E.H.L., van osterhout, J.J.G.M.: On-line signature verification with hidden Markov Models. In: Fourteenth International Conference on Pattern Recognition, 20 August 1998
Wan, L., Wan, B., Lin, Z-C.: On-line signature verification with two-stage statistical models. In: Eighth International Conference on Document Analysis and Recognition (ICDAR 2005), 31 August 2005
Wan, L., Wan, B., Lin, Z-C.: On-line signature verification with two stage statistical models. In: Eighth International Conference on Document Analysis and Recognition (ICDAR 2005), pp. 282–286 (2005)
Hatkar, P.V., Prof Salokhe, P.T., Malgave, A.A.: Handwritten signature verification using neural network. In: International Journal of Innovations in Engineering Research and Technology [IJIERT], vol. 2, no. 1, January 2015
Gruber, C., Gruber, T., Krinninger, S., Sick, B.: Online signature verification with support vector machines based on LCSS kernel functions. IEEE Trans. Sys. Man Cybern. - Par: B Cybern. 40(4), 1088–1100 (2010)
Iranmanesh, V., Ahmad, S.M.S., Adnan, W.A.W., Yussof, S., Arigbabu, O.A., Malallah, F.L.: Online handwritten signature verification using neural network classifier based on principal component analysis. The Scientific World Journal 2014 Article
Hassaïne, A., Al-Maadeed, S.: An online signature verification system for forgery and disguise detection. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012. LNCS, vol. 7666, pp. 552–559. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34478-7_67
Feng, H., Wah, C.C.: Online signature verification using a new extreme points warping technique. Elsevier 24(16), 2943–2951 (2003)
Afsar, A., Arif, M., Farrukh, U.: Wavelet transform based global features for online signature recognition. In: Pakistan Section Multitopic Conference. Karachi, pp. 1–6 (2005)
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Meena, L., Chaurasiya, V.K., Purohit, N., Singh, D. (2021). Comparison of SVM and Random Forest Methods for Online Signature Verification. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12616. Springer, Cham. https://doi.org/10.1007/978-3-030-68452-5_30
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