Abstract

As a result of the urgent need to immediately identify individuals through the Internet, especially given the Coronavirus (COVID-19) pandemic at present, the recognition of online handwritten signatures has quickly evolved to become an urgent and necessary matter. However, signature identification remains challenging in the pattern recognition field due to intra-class variability and inter-class similarity. Intra-class variability is a characteristic of human behavioural activities, particularly in handwriting where no two handwritten signatures of any person can exactly coincide. The inter-class similarity is also a characteristic of human movement-based activities such as handwritten signatures particularly when the number of writers is large. In this research, an optimized transfer-learning-based architecture is proposed as a highly accurate identification technique for online-signatures using ResNet18 as a feature extraction deep-learning module. The X-Y time-series signals of the signatures were initially converted into images and used in retraining the ResNet18 model to achieve relatively high accuracy. The retrained ResNet18 model was then used to extract features that possess high discriminative distances among different classes of handwritten signatures. The model’s deep layers were searched to determine the best layer that provided the most discriminative features when using a 1-nearest neighbour learning algorithm based on the cosine distance. By using an ensemble of five models trained on rotated versions of the original signatures and using only three training samples from each writer, the classification accuracy achieved 100% when applied on the genuine signatures of public datasets such as SVC 2004 TASK1 and TASK2, and a new proprietary dataset composed of 120 genuine users. When the abovementioned technique was tested on the aggregated version of the aforementioned datasets, the resultant accuracy was still above 99%. Moreover, the robustness of the technique was proven by testing the generated models trained with one dataset with the other two datasets resulting in accuracy above 99% for all combinations.

Highlights

  • The development and spread of e-commerce systems and the abrupt rise in online commercial and managerial transactions resulting from the present Coronavirus (COVID-19) pandemic have caused online signature recognition and verification to be one of the top requirements to support online identification and authentication

  • The third difficulty associated with signature classification is the small number of training samples per user that can be practically available to build the trained model of the recognition system; in building deeplearning-based models [2]

  • Based on the above discussion and background, this study aims to address the online handwritten signature identification (OHSI) challenges and to meet the substantial needs for generalization of the OHSI by examining the features produced by different layers of a deep residual network and the best distance to use in the discrimination among the signatures of different writers

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Summary

INTRODUCTION

The development and spread of e-commerce systems and the abrupt rise in online commercial and managerial transactions resulting from the present Coronavirus (COVID-19) pandemic have caused online signature recognition and verification to be one of the top requirements to support online identification and authentication. The handwritten signature recognition technique, when combined with another biometric test, could be used for identification and authentication tasks. Based on the above discussion and background, this study aims to address the OHSI challenges and to meet the substantial needs for generalization of the OHSI by examining the features produced by different layers of a deep residual network and the best distance to use in the discrimination among the signatures of different writers. Our contributions are summarized in the conclusion section of this paper

BRIEF SURVEY
THE ARCHITECTURE OF THE PROPOSED TECHNIQUE
TECHNIQUE ENHANCEMENTS AND EVALUATION
Findings
VIII. CONCLUSION
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