Abstract
Signature Verification is an essential and challenging task in forensic document analysis. Signature Verification is a process where the authenticity of a given signature is validated; i.e. it is confirmed, after analysis, whether the given signature is forged or not. The objective of the work proposed here is to create a new algorithm that can be used for verification of signatures used in cheques and various legal documents for customer authentication in banking systems. The proposed algorithm is designed using Daubechies complex wavelet transform (DCxWT) which has superior approximate shift invariant and edge representation properties over real valued wavelet transform. This is what motivates the use of DCxWT in the Signature Verification task. For classification of valid and forged signatures we have used Support Vector Machine (SVM) classifier. We have used Mutual Information kernel that is an alternative kernel of the KL divergence kernel in SVM classifier. Proposed method is compared with KL-divergence based kernel and some other standard kernels like Poly, RBF, and Tanh kernel of SVM Classifier. The method we proposed is juxtaposed with different state-of-the-art methods and effectiveness of the algorithm is also measured using different quantitative performance measures.
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Desai, A., Mashruwala, V., Khare, M. (2021). Handwritten Signature Verification Using Complex Wavelet Transform and Mutual Information Based Kernel. In: Abraham, A., et al. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020). SoCPaR 2020. Advances in Intelligent Systems and Computing, vol 1383. Springer, Cham. https://doi.org/10.1007/978-3-030-73689-7_27
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DOI: https://doi.org/10.1007/978-3-030-73689-7_27
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