Writer Dependent Offline Signature Verification Based on Cluster-Specific Classifier

In this paper, we propose a writer dependent approach for offline signature verification based on writer-specific features and cluster-specific classifiers. In this work, writer-dependency is exploited at three levels: features, classifiers, and clusters. Initially, a template signature is selected for each writer from the training samples of that writer. This template signature serves as a representative signature of the respective writer. The relevant features for each writer are chosen using a filter-based feature selection method. The writers are then clustered based on their similar characteristics using the k-means algorithm. After clustering, a cluster-specific classifier is identified. This classifier is then set as the default classifier for all the writers in that cluster. During verification, writer-specific features and cluster-specific classifiers of the claimed writer are used to verify the authenticity of the given test signature. The approach is verified on three benchmarking offline signature datasets: CEDAR, MCYT, and GPDS-960.