This paper explores the utility of information derived from the dynamic time warping (DTW) cost matrix for the problem of online signature verification. The prior works in literature primarily utilize only the DTW scores to authenticate a test signature. To the best of our knowledge, the characteristics of the warping path (used for the alignment) in the cost matrix is hardly exploited for verification of online signatures. Accordingly, we devise a score that utilizes the information from the cost matrix and warping path alignments. We subsequently consider its fusion (using a sum rule combiner) with the DTW score for authenticating the veracity of a test signature. In addition, a minor modification is suggested with regards to the set of features employed for matching the signatures. We introduce a spacing parameter for feature extraction and demonstrate its applicability in increasing the separation between the distribution of genuine and forgery signatures for an user. Our method has been tested on two publicly available online signature databases namely the SVC-2004 Task 2 and MCYT-100. We report reduction in error rates over the traditional DTW framework.
In the past decade, corporations are increasingly engaging in efforts whose aim is the analysis and wide-ranging use of big data. The majority of academic big data articles have been focused on methods, approaches, opportunities, and organizational impact of big data analytics. In this article, the focus is on the ability of big data (while acting as a direct source for impactful analysis) to also augment and enrich the analytical power of data warehouses.
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