Paper Session - Workshop Papers : Paper #2 (10:20 - 10:24)

Paper Title: OSVGAN: Generative Adversarial Networks for Data Scarce Online Signature Verification

Authors: Chandra Sekhar Vorugunti, Sai Sasikanth Indukuri, Viswanath Pulabaigari and Rama Krishna Sai Gorthi

Email: Chandrasekhar.v@iiits.in, sindukuri@umass.edu, viswanath.p@iiits.ac.in, rkg@iittp.ac.in

Short Description: In this work, two most challenging requirements of Online Signature Verification (OSV) are addressed. First, data scarcity to thoroughly test the framework for real time deployment in critical applications. Second, achieving few shot learning, especially one-shot learning to classify the genuineness of test signature with as minimum as one training sample per user.

Keywords: Synthetic Online Signature generation, Generative Adversarial Networks, Online Signature verification.

Abstract : Impractical to acquire a sufficient number of signatures from the users and learning the inter and intra writer variations effectively with as minimum as one training sample are the two critical challenges need to be addressed by the Online Signature Verification (OSV) frameworks. To address the first challenge, we are generating writer specific synthetic signatures using Auxiliary Classifier GAN, in which a generator is trained with a maximum of 40 signature samples per user. To address the second requirement, we are proposing a Depth wise Separable Convolution based Neural Network, which results in achieving one shot based OSV with reduced parameters. A first of its kind of experimental analysis is done with an increased set of signature samples (five-fold) on two widely used datasets SVC, MOBISIG. The state-of-the-art outcome in almost all categories of experimentation confirms the competence of the proposed OSV framework and qualifies for the real time deployment in limited data applications.