Development of a System with Online Signature Biometric for Access Control Application

Abhisek HAZRA, Amit CHAUDHURI, Asok BANDYOPADHYAY

Abstract


Online signature verification system is a technique based on behavioural biometrics which is gaining popularity for its widespread acceptability due to ease of use and forge proof features. Online (dynamic) signatures are captured from pressure sensitive tablets, signature pads, tablet PCs for further processing. This paper depicts the working mechanism of such a system capable of verifying the true signers as well as detecting the forgery attempt. After the signature data collection, the signatures were preprocessed for future use. A set of total 58 different features (local, global and normalized) were extracted from the online signature data. The system presents a binary classification technique since the test signature is categorized into one of the two classes (genuine or false). Template matching based Dynamic Time Warping (DTW) classifier is used for training and testing purposes with an average recognition rate of 90% for true signers. A separate version of the system was also developed which blocks random and skilled forgeries with a high level of accuracy though the TN rate is higher than the system described here.

Keywords


Signature Verification, Behavioural Biometric, Access Control, Template Matching, Machine Learning.


DOI
10.12783/dtcse/ica2019/30720

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