Large-scale offline signature recognition via deep neural networks and feature embedding - Calik, Nurullah; Kurban, Onur Can; Yilmaz, Ali Riza; Yildirim, Tulay; Ata, Lutfiye Durak (ELSEVIER, 2019)
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. In many studies, 10 or more signatures are used for training phase, which is mostly intractable in practice. 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. 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 addition to these, by replacing the activation function rectifier linear unit (ReLU) with leaky ReLU, we create a new network structure called LS2Net _ v2. 96k signatures obtained from 4k signers of GPDS-4000 dataset are used. For a realistic comparison, MCYT and CEDAR are chosen besides the GPDS dataset. The proposed networks are compared with Visual Geometry Group (VGG)-[S, M, 16], which are the frequently used networks in the literature. The networks are tested with two splitting ratios as 25% train - 75% test and 50% train - 50% test per signers. For each ratio, five train and test subsets are randomly generated. Performance metrics are obtained by averaging the results of these five subsets. LS2Net achieved 96.41% and 98.30% accuracy performance for the 25%-75% ratio in MCYT and CEDAR. Moreover, LS2Net_v2 achieves best results by getting 96.91% accuracy score for 25%-75% ratio for GPDS-4000. Batch normalization and C3 algorithm contribute to the performance significantly. (C) 2019 Elsevier B.V. All rights reserved.
Keywords: Large-scale dataset; Signature recognition; Convolutional neural network; Batch normalization; Deep learning; VERIFICATION; TRANSFORM; MODEL; Computer Science