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The augmented usage of deep learning-based models for various AI relatedproblems are as a result of modern architectures of deeper length and theavailability of voluminous interpreted datasets. The models based on thesearchitectures require huge training and storage cost, which makes theminefficient to use in critical applications like online signature verification(OSV) and to deploy in resource constraint devices. As a solution, in thiswork, our contribution is two-fold. 1) An efficient dimensionality reductiontechnique, to reduce the number of features to be considered and 2) astate-of-the-art model CNN-LSTM based hybrid architecture for online signatureverification. Thorough experiments on the publicly available datasets MCYT,SUSIG, SVC confirms that the proposed model achieves better accuracy even withas low as one training sample. The proposed models yield state-of-the-artperformance in various categories of all the three datasets.

A Light weight and Hybrid Deep Learning Model based Online Signature Verification