Reference selection for offline writer-dependent signature verification using a Siamese convolutional neural network

Ming-I Lo; Tsung-Yu Lu; Er-Hao Chen; Yeong-Luh Ueng

doi:10.1117/12.2601720

30 June 2021 Reference selection for offline writer-dependent signature verification using a Siamese convolutional neural network

Ming-I Lo, Tsung-Yu Lu, Er-Hao Chen, Yeong-Luh Ueng

Author Affiliations +

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 1187823 (2021) https://doi.org/10.1117/12.2601720
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

This paper focuses on offline writer-dependent signature verification using a Siamese convolutional neural network. A Siamese network is comprised of equal-weighted twin networks that can be trained to learn a feature space in which similar observations are juxtaposed. To reduce the impact of the high intra-variability of the signature and ensure that the Siamese network is able to learn more effectively, we propose a method of selecting a Reference (REF). Using the proposed reference selection, the accuracy can be increased by 6.4%. By utilizing the GPDS-160 signature data-set, the designed system is able to achieve an accuracy of 94.5%, which is a better result than that achieved by current state-ofthe-art writer-dependent techniques.

Citation Download Citation

Ming-I Lo, Tsung-Yu Lu, Er-Hao Chen, and Yeong-Luh Ueng "Reference selection for offline writer-dependent signature verification using a Siamese convolutional neural network", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 1187823 (30 June 2021); https://doi.org/10.1117/12.2601720

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