DeepHSV: UserIndependent Offline Signature Verification Using TwoChannel CNN Offline handwritten signature verification has practical significance in financial, administrative and judicial field. Traditional methods rely on handcrafted local and global feature descriptors. Recently, researchers start to apply convolutional neural network (CNN) to learn representative features towards this task. However, most of these methods suffer from overfitting. In this paper we propose a novel twochannel CNN network, namely 2Channel2Logit (2C2L), to address this issue. The input to the network is the concatenation of reference and query signatures. The output of convolutional layers are two logits that measure the similarity between reference and query signatures. We explicitly add dropout layers and a 2Logit layer to make the network less prone to overfitting issues. The proposed method uses only one reference signature for testing and the training system is independent from the queried user. Experiments on the latest GPDSSynthetic database demonstrate that the proposed method can significantly improve verification accuracy. We improve the performance by a large margin, from 22.24% in stateoftheart to 9.95% in terms of equal error rate (EER).

Online Signature Verification by FewShot Separable Convolution Based Deep Learning Online Signature Verification (OSV) is a widely used biometric feature for recognized and authorized technique to authenticate a writersu0027s distinctiveness and behavioral characteristic. Owing to huge intraindividual changeability, OSV is a challenging problem. Usage of online signatures in mcommerce and mpayment demands for light weight frameworks to classify a signature. The recent OSV models grounded on convolutional neural networks (CNN) and its variants are heavy weight and computationally intensive due to higher amount of parameters to learn. In this context, we put forward a CNN centric OSV framework which uses a stack of depthwise separable (DWS) convolution layers, which makes the framework light weight and enables the few shot learning for signature verification with quite higher accuracy compared to conventional deep learning models. To prove the robustness of our proposed framework, we performed exhaustive experimental evaluations with three standard datasets i.e. MCYT100 (DB1), SUSIGVisual corpus and SVC2004Task2. Experimental results confirm the efficiency of depthwise separable convolutions grounded OSV by realizing a lesser error rate as related to various current and stateofthe art OSV frameworks.