Information Divergence-Based Matching Strategy for Online Signature Verification

The phenomenon of data clutter caused by intervariability (individual features) and intravariability (intrinsic noise of reference samples) is one of the most important reasons for performance degradation in online signature verification systems. To address this problem, we introduce information divergence in the field of signature verification to take full advantage of the information contained in all reference samples by shifting the distance measurement between the test and reference samples to a similarity measurement between two distributions (generated between reference samples as well as between the test sample and all reference samples) and simultaneously make full use of the spatial information of the reference samples. Based on that change, we propose a novel information divergence framework and provide some matcher instances that work within the proposed framework to effectively improve the performance of a signature verification system. Furthermore, to exploit the advantages of the new matching strategy, we propose a new dynamic time warping algorithm. In addition, we provide in-depth analysis of several distance normalizations and apply them to signature verification to reduce the adverse effect of “clutter” on signature data, which can effectively improve system performance. The experimental results on the MCYT-100 and SUSIG signature databases achieved equal error rates of 2.25% and 1.70% when 10 reference samples were used and 3.16% and 2.13% when 5 reference samples were used, respectively, illustrating the effectiveness of the proposed strategy in relation to other state-of-the-art strategies.