Multi‐task learning using GNet features and SVM classifier for signature identification

Abstract

Signature biometrics is a widely accepted and used modality to verify the identity of an individual in many legal and financial organisations. A writer and language-independent signature identification method that can distinguish between the genuine and forged sample irrespective of the language of the signature has been proposed. To extract the distinguishing features, a pre-trained model GoogLeNet, which is fine-tuned with the largest signature dataset present till date (GPDS Synthetic), has been used. The proposed method is tested over the BHSig260 (contains images from two regional languages, Bengali and Hindi) dataset. With the help of the above fine-tuned model, knowledge is transferred to the publicly available datasets – BHSig260 and MCYT-75. The features extracted using the fine-tuned model has been fed to the support vector machine (SVM) classifiers. With the proposed method, 96.5% and 95.7% accuracy on Bengali and Hindi datasets, and 93% on MCYT-75 with skilled forged samples have been achieved respectively.