Authentication based on pole-zero models of signature velocity

Abstract

With the increase of communication and financial transaction through internet, on-line signature verification is an accepted biometric technology for access control and plays a significant role in authenticity and authorization in modernized society. Therefore, fast and precise algorithms for the signature verification are very attractive. The goal of this paper is modeling of velocity signal that pattern and properties is stable for persons. With using pole-zero models based on discrete cosine transform, precise method is proposed for modeling and then features is founded from strokes. With using linear, parzen window and support vector machine classifiers, the signature verification technique was tested with a large number of authentic and forgery signatures and has demonstrated the good potential of this technique. The signatures are collected from three different database include a proprietary database, the SVC2004 and the Sabanci University signature database benchmark databases. Experimental results based on Persian, SVC2004 and SUSIG databases show that our method achieves an equal error rate of 5.91%, 5.62% and 3.91% in the skilled forgeries, respectively.

DeepFuseOSV: online signature verification using hybrid feature fusion and depthwise separable convolution neural network architecture

“…As exhibited in Tables 5–7, regardless of the systems proposed in [2, 5, 9, 11, 12, 25, 35, 43] is yielding better EER values contrasted with the proposed framework, the essential pitfall with these models is that these models are assessed with only one category, i.e. S_01, R_01.…”

“…Whereas, the proposed model is thoroughly evaluated with wide experimentation in Skilled 1, 5, 10, 15, 20 and Random 1, 5, 10, 15, 20. Hence, its superiority is validated as compared to [2, 5, 9, 11, 12, 25, 35, 43].…”

The Optimal Model for Copy-Move Forgery Detection in Medical Images

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