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

Hao Bian; Fangjun Luan; Shuai Yuan

doi:10.1117/12.2656745

20 October 2022 Online signature verification based on attention mechanism depth-wise separable convolution residual network

Hao Bian, Fangjun Luan, Shuai Yuan

Author Affiliations +

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124513S (2022) https://doi.org/10.1117/12.2656745
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

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.

Citation Download Citation

Hao Bian, Fangjun Luan, and Shuai Yuan "Online signature verification based on attention mechanism depth-wise separable convolution residual network", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124513S (20 October 2022); https://doi.org/10.1117/12.2656745

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