Online signature verification based on attention mechanism depth-wise separable convolution residual network

Abstract

How to adaptively learn important signature features and use a lightweight model to achieve high-precision signature verification is still a challenge in the field of online signature verification. In response to this challenge, we proposed an attention mechanism depth-wise separable convolution residual network(A-DWSRNet) for online signature verification. First of all, the weight of signature features is adaptively learned through the convolutional attention module to improve the representation learning ability of the network. Next, the depth-wise separable convolution and depth convolution modules are introduced to improve the standard residual structure to construct a depth-wise separable residual unit, which reduces the overall parameter amount of the model and alleviates the loss of feature information of the multi-step residual structure. Finally, the proposed method has achieved 2.88% equal error rate (EER) and 4.17% EER in MCYT100 database and SVC2004-task2 database, respectively. The results show that the proposed method can effectively improve the accuracy of the Online signature verification system.