COMPOSV: compound feature extraction and depthwise separable convolution-based online signature verification

Abstract

Online signature verification (OSV) is a predominantly used verification framework, which is intended to authenticate the legitimacy of a test signature by learning the writer specific signing characteristics. The significant adoption of OSV in critical applications like E-Commerce, M-Payments, etc., emphasizes on a framework which addresses critical requirements: (1) The framework should be competent to classify a test signature with few training samples, as minimum as one per user and with the least number of features extracted per signature, and (2) The framework should accurately classify a test signature of an unseen user. Even though several OSV frameworks are proposed based on various advanced techniques, still there is a necessity for a holistic OSV framework which is able to accomplish the abovementioned requirement criteria. To realize the above requirements, we present a depthwise separable (DWS) convolution-based OSV framework which facilitate the classification of test signature samples from an unseen user. In addition to this, we introduce a novel dimensionality reduction-based feature extraction technique, which decrease the dimensionality of a set of features from 100 to 3 concerning to MCYT-330, MCYT-100 and 47 to 3 with regard to SVC, SUSIG datasets. To appraise the competence of our proposed COMPOSV framework, extensive experiments and ablation studies are conducted on four widely used datasets, i.e., MCYT-100, MCYT-330, SVC and SUSIG. The proposed framework, trained with signature samples of only 10% of users (seen), can classify the signatures of 90% of unseen users with higher accuracy than the frameworks trained with signature samples of all users.