Impact of Type of Convolution Operation on Performance of Convolutional Neural Networks for Online Signature Verification

Abstract

An Online signature is a multivariate time series, a commonly used biometric source for user verification. Deep learning (DL) is increasingly becoming ubiquitous as a paradigm for solving problems that come with a wealth of data. Convolution has been its main workhorse. Recently, DL had marked its entry in online signature verification (OSV), a standard bio-metric method that has been mostly dealt with in traditional settings. However, embracing a DL solution to a problem requires certain issues to be tackled, viz. (i) type of convolution, (ii) order of convolution, and (iii) input representation. In this work, we experimentally analyse each of the issues mentioned above regarding OSV, and subsequently present a superior model that reports state-of-the-art (SOTA) performance on three widely used data-sets namely MCYT-100, SVC, and Mobisig. Specifically, the proposed model reports an equal error rate (EER) of 9.72% and 3.1% in Skilled_01 categories of MCYT-100 and SVC data-sets, with gains of around 4% and 3% over the next best performing methods, respectively. The experimental outcome confirms that the interrelationship between the type and order of convolution operation and the input signature representation plays a significant role in the performance of OSV frameworks