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A Dempster–Shafer theory based classifier combination for online Signature recognition and verification systems

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Abstract

With the advancement in technology, the society demands a robust method for person authentication. Traditional authentication methods are based on the person’s knowledge such as PIN, passwords, and tokens etc. However, such methods are prone to steal and forgotten risks. Therefore, an efficient method for person identification and verification is required. In this paper, we present a novel biometric approach for online handwritten signature recognition and verification using Dempster–Shafer theory (DST). DST has been used effectively for combination of different information sources which provide incomplete, and complementary knowledge. Initially, signature identification and verification processes have been carried out using two different classifiers, namely, Hidden Markov Model (HMM) and Support Vector Machine (SVM). Next, the performance in terms of accuracy and the reliability of the system has been increased using DST by combining the probabilistic outputs of SVM and HMM classifiers. The feasibility of the approach has been tested on MCYT DB1 and SVC2004 biometric public databases for Latin script and a new online signature dataset for Devanagari script. To our knowledge there exist no dataset on online signature available in Devanagari script. Experimental results shows that the present approach is efficient in recognition and verification of signatures and outstrips existing work in this regard till date.

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Notes

  1. https://sites.google.com/site/iitrcsepradeep7/.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, NIT Patna, Patna, India

    Rajib Ghosh

  2. Department of Computer Science and Engineering, IIT Roorkee, Roorkee, India

    Pradeep Kumar & Partha Pratim Roy

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  1. Rajib Ghosh
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  2. Pradeep Kumar
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Ghosh, R., Kumar, P. & Roy, P.P. A Dempster–Shafer theory based classifier combination for online Signature recognition and verification systems. Int. J. Mach. Learn. & Cyber. 10, 2467–2482 (2019). https://doi.org/10.1007/s13042-018-0883-9

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