1 research outputs found
A Light weight and Hybrid Deep Learning Model based Online Signature Verification
The augmented usage of deep learning-based models for various AI related
problems are as a result of modern architectures of deeper length and the
availability of voluminous interpreted datasets. The models based on these
architectures require huge training and storage cost, which makes them
inefficient to use in critical applications like online signature verification
(OSV) and to deploy in resource constraint devices. As a solution, in this
work, our contribution is two-fold. 1) An efficient dimensionality reduction
technique, to reduce the number of features to be considered and 2) a
state-of-the-art model CNN-LSTM based hybrid architecture for online signature
verification. Thorough experiments on the publicly available datasets MCYT,
SUSIG, SVC confirms that the proposed model achieves better accuracy even with
as low as one training sample. The proposed models yield state-of-the-art
performance in various categories of all the three datasets.Comment: accepted in ICDAR-WML: The 2nd International Workshop on Machine
Learning 201