Reference selection for offline writer dependent signature verification using a Siamese convolutional neural network
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.
International Conference on Digital Image Processing