Impact of augmentation methods in online signature verification
The aim of this paper is to investigate the impact of selected data augmentation techniques on the learning performance of neural networks for dynamic signature verification. The paper investigates selected data augmentation techniques in deep learning for verification purpose of dynamic signature....
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | Colombia |
| Institución: | Corporación Universidad de la Costa |
| Repositorio: | Repositorio REDICUC |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.cuc.edu.co:11323/10733 |
| Acceso en línea: | https://hdl.handle.net/11323/10733 https://repositorio.cuc.edu.co/ |
| Access Level: | acceso embargado |
| Palabra clave: | Signature Online signature Biometrics Verification Augmentation |
| Sumario: | The aim of this paper is to investigate the impact of selected data augmentation techniques on the learning performance of neural networks for dynamic signature verification. The paper investigates selected data augmentation techniques in deep learning for verification purpose of dynamic signature. Two neural networks were used as classifiers: MLP and LSTM-FCN. Investigation of five selected augmentation methods and experiments were performed on the open source signature database SVC2004. The authors tested both classifiers without augmentation and then with data augmentation for three extensions of the learning set and three sizes of the user database. They presented the results of the experiments in tabular form for each augmentation method. The results were compared with the existing dynamic signature verification methods and given in the paper. |
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