Interval-valued symbolic representation based method for off-line signature verification
Pal, S, Alaei, A, Pal, U & Blumenstein, M 2015, 'Interval-valued symbolic representation based method for off-line signature verification', in Proceedings of the International Joint Conference on Neural Networks (IJCNN), Kilarney, Ireland, 11-16 July, IEEE, USA. ISBN: 9781479919604.
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The objective of this investigation is to present an interval-symbolic representation based method for offline signature verification. In the feature extraction stage, Connected Components (CC), Enclosed Regions (ER), Basic Features (BF) and Curvelet Feature (CF)-based approaches are used to characterize signatures. Considering the extracted feature vectors, an interval data value is created for each feature extracted from every individual's signatures as an interval-valued symbolic data. This process results in a signature model for each individual that consists of a set of interval values. A similarity measure is proposed as the classifier in this paper. The interval-valued symbolic representation based method has never been used for signature verification considering Indian script signatures. Therefore, to evaluate the proposed method, a Hindi signature database consisting of 2400 (100×24) genuine signatures and 3000 (100×30) skilled forgeries is employed for experimentation. Concerning this large Hindi signature dataset, the highest verification accuracy of 91.83% was obtained on a joint feature set considering all four sets of features, while 2.5%, 13.84% and 8.17% of FAR (False Acceptance Rate), FRR (False Rejection Rate), and AER (Average Error Rate) were achieved, respectively.