Innovative Outlier Removal Techniques to Enhance Signature Authentication Accuracy for Smart Society

Abstract

A smart society is an empowered society, which can improve the lives of its citizens by using the latest innovations and technologies. This improvement can happen in several dimensions out of which security is a major one. Inconsistency and forgery are very common phenomenon where handwritten signatures are often preserved for training a classifier to authenticate a person. The removal of outliers, at the outset, obviously improves the quality of training and the classifier. The present article deals with the mechanized segregation of the poor-quality authentic signatures from reliable ones. Machine learning algorithms for outlier handling utilizing clustering, classification and statistical techniques have been implemented in this context. Subsequent performance evaluation after outlier removal reflects improvement of both true positive and true negative recognition rate accuracy. The performance evaluation presents the significant differences between authentication accuracy and forgery accuracy in the context of building a safe, secure and smart society.