Biometric Authentication Using Online Signatures

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Abstract

We overview biometric authentication and present a system for on-line signature verification, approaching the problem as a two-class pattern recognition problem. During enrollment, reference signatures are collected from each registered user and cross aligned to extract statistics about that user’s signature. A test signature’s authenticity is established by first aligning it with each reference signature for the claimed user. The signature is then classified as genuine or forgery, according to the alignment scores which are normalized by reference statistics, using standard pattern classification techniques. We experimented with the Bayes classifier on the original data, as well as a linear classifier used in conjunction with Principal Component Analysis (PCA). The classifier using PCA resulted in a 1.4% error rate for a data set of 94 people and 495 signatures (genuine signatures and skilled forgeries).