Signature verification using geometrical features and artificial neural network classifier
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ORIGINAL ARTICLE
Signature verification using geometrical features and artificial neural network classifier Anamika Jain1 • Satish Kumar Singh1 • Krishna Pratap Singh1 Received: 10 April 2020 / Accepted: 26 October 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Signature verification has been one of the major researched areas in the field of computer vision. Many financial and legal organizations use signature verification as an access control and authentication. Signature images are not rich in texture; however, they have much vital geometrical information. Through this work, we have proposed a signature verification methodology that is simple yet effective. The technique presented in this paper harnesses the geometrical features of a signature image like center, isolated points, connected components, etc. and, with the power of artificial neural network classifier, classifies the signature image based on their geometrical features. Publicly available dataset MCYT, BHSig260 (contains the image of two regional languages Bengali and Hindi) has been used in this paper to test the effectiveness of the proposed method. We have received a lower equal error rate on MCYT 100 dataset and higher accuracy on the BHSig260 dataset. Keywords Behavioral biometric Geometrical features ANN Signature
1 Introduction Biometric plays a vital role in the authentication of an individual in many financial institutions, and signatures are the most widely used modality for this purpose. Biometrics is used to identify or verify an individual digitally. The security applications have been used in many financial and educational institutions using biometrics technology for decades [17]. Biometrics are classified into two categories: physiological and behavioral [13]. Physiological biometrics includes face, iris, fingerprint, etc., and behavioral biometrics has signature, gait, etc. Authentication of the signatures is carried out manually and highly dependent on the mood of the verifier. Owing to its importance and unavailability of efficient offline & Anamika Jain [email protected] Satish Kumar Singh [email protected] Krishna Pratap Singh [email protected] 1
verification methods, we have utilized signatures in this experiment [21, 24]. Depending upon the acquisition process, signature biometric is categorized into two modes: online and offline. In online mode, signatures are collected using tablets and electronic pads and have auxiliary information like angle, pressure, pen up/down, etc. On the other hand, in offline mode the signatures are acquired on the sheet of paper with writing instruments. Later these sheets are digitized using the scanner and cropped to the signature content [22]. These types of signature do not have any supportive information, and this makes the offline mode of the signature a challenging problem. Signatures are easy to spoof with some practice, and this makes them vulnerable to forgery [1]. There are two significant types of
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