On-Line Signature Verification Using Symbolic Aggregate Approximation (SAX) and Sequential Mining Optimization (SMO)

Signatures are the single most widely used method of identifying an individual but they carry with them an alarmingly significant number of vulnerabilities, implying the need for an effective and robust method of precisely identifying an individual´s signature. The signature of an individual is visually acquired by using a pen-based tracking system [1], [2]. This paper considers the possibility of discretizing visually acquired signatures to represent them as an unordered collection of words and then use Sequential Mining Optimization (SMO) for training Support Vector Machines (SVM) to classify the signature as either legitimate or forged. Signature discretization is done using Symbolic Aggregate Approximation (SAX) [4]. SAX reduces the dimensions of the signature and produces a list of SAX words that are then represented as a bag-of-patterns model for classification purposes [6]. The approach was tested on a dataset of 3960 signatures of 106 subjects distributed across two sets. The results show good classification accuracy bolstering the real time application of the methodology.