Handwritten signature verification is a behavioral biometric. Every day, we may face signature verification problem directly or indirectly whether it is in a banking transaction or signing a credit card transaction or authenticating a legal document. In order to solve this problem, during the last few decades, research has been going on with different approaches to introduce an efficient signature verification and identification system. This paper presents some basic concepts of signature and also explores on different approaches for verification. General TermsPattern Recognition and Security.

An offline handwritten signature verification system reduces number of occurrences of fraud events. In different document materials like credit cards, passport validation, banking transactions and different financial transactions, signatures are verified. Whether a signature is genuine or forgery is detected by comparing the training and testing data sets. This paper describes an offline handwritten signature verification system using global features. Here, signature verification is done by Euclidean distance which results False Rejection Rate (FRR), False Acceptance Rate (FAR) and Total Success Rate (TSR) as 6.66%, 26.66% and 93.3% respectively.

In the field of security and forgery prevention, handwritten signatures are the most widely recognized biometric since long and also most practical. Although handwritten signature verification systems are studied using both On-line and Off-line approaches, Off-line signature verification systems are more difficult to compare to On-line verification systems. This is due to the lack of dynamic information, viz. a database which constantly stores the latest signature of the person.  In the paper an approach using ensemble methods are adopted to classify a signature as forgery or not. In proposed system, three classifiers, viz, one unsupervised, viz. Fuzzy C-Means (FCM) and two supervised classifiers, viz. Naive Bayes (NB) and Support Vector Machine (SVM) are used as base classifiers. An attempt is made to compare the different approaches. We attempt both the categories of classification not only because both of them are applicable in this particular case but also with an objective of finding out which performs better. In most cases it is observed that Naive Bayes and Ensemble are comparable as they exhibit better performance than the other two. But among them, in most of the cases Ensemble classifier performs better than the Naive Bayes and consequently we have taken the Ensemble as a final classifier.