Graph

Pattern recognition (psychology)

Structural engineering

Data mining

Machine learning

Engineering

Artificial intelligence

Word error rate

Computer science

Theoretical computer science

Artificial neural network

Node (physics)

Convolutional neural network

Journal of Ambient Intelligence and Humanized Computing

Recent years have witnessed the surge of graph neural networks (GNNs) in various research fields. Compared to the convolutional neural networks (CNNs), GNNs happen to be more powerful and comprehensive as they use graphs to learn an embedding that contains information about the neighbourhood of a target node in a graph. Hence, GNN models have achieved exemplary performance in many tasks such as node classification, link prediction, and graph classification. In this work, we have come up with a solution to the problem of offline signature verification with the relational inductive biases of a GNN model. To apply the node classification strategy of a GNN model, a network is constructed. The network consists of training signature samples of a particular user, where each node represents a signature sample. GraphSAGE, an inductive representation learning algorithm, is utilized to train the model. Later, a test signature sample (i.e., a newly added node to the existing training graph) is classified into one of the two classes: genuine or forged using the trained model. The obtained results lay out a robust manifestation of the benefits of learned gray level co-occurrence matrix (GLCM) based features. The proposed method has been evaluated on two standard signature datasets, namely MCYT-75 and UTSig. The equal error rate (EER) values are 0.13 and 0.66 on MCYT-75 and UTSig datasets respectively. Use of a writer dependent schema with a distinctive model for each writer, and only with a handful number of training samples, the proposed model stands out amongst its predecessors.

Offline signature verification system: a graph neural network based approach