Abstract
This work aims at proposing an approach for verification of offline signature based on user-dependent features and classifiers. We have used 50 global features of shape, geometric, and texture category. The features suitable for each user are selected by means of a computationally efficient filter-based feature selection method. In addition, user dependency has been considered at classifier level also. Based on lowest equal error rate obtained with training samples, the decision is made on the features and classifier to be used for a user. In the first stage, we conducted experiments without any feature selection but with user-dependent classifier. Further, experiments have been carried out under varying number of features for all users with user-dependent classifiers. To evaluate the performance of the proposed model, experimentation has been conducted on MCYT offline signature dataset which is one of the standard benchmark datasets. The EER that we obtained indicates the effectiveness of the usage of writer-dependent characteristics.
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Manjunatha, K.S., Annapurna, H., Guru, D.S. (2019). Offline Signature Verification: An Approach Based on User-Dependent Features and Classifiers. In: Nagabhushan, P., Guru, D., Shekar, B., Kumar, Y. (eds) Data Analytics and Learning. Lecture Notes in Networks and Systems, vol 43. Springer, Singapore. https://doi.org/10.1007/978-981-13-2514-4_20
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DOI: https://doi.org/10.1007/978-981-13-2514-4_20
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