![]() 96k signatures obtained from 4k signers of GPDS-4000 dataset are used. In addition to these, by replacing the activation function rectifier linear unit (ReLU) with leaky ReLU, we create a new network structure called LS2Net_v2. Moreover, we present, Class Center based Classifier (C3) algorithm, which relies on 1-Nearest Neighbor (1-NN) classification task by using the class-centers of the feature embeddings obtained from fully-connected layers. In this study we propose a new convolutional neural network (CNN) structure named Large-Scale Signature Network (LS2Net) with batch normalization to deal with the large-scale training problem. In many studies, 10 or more signatures are used for training phase, which is mostly intractable in practice. Although there have been several developments in offline signature recognition, there is still no much focus on the recognition problem using a small sample size for the training.
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