ON-LINE SIGNATURE VERIFICATION USING MULTIRESOLUTION FEATURE EXTRACTION AND SELECTION

Abstract

Handwritten signatures are a common behavioral biometric. They are widely accepted for identification purposes, such as approbating and authenticating legal documents and financial contracts. The main challenge of signature verification is the high dimensionality of the signature features dataset that makes the corroboration procedure computationally costly. In this paper, we reduced the dimension of the input data with almost no loss of information. To this end, wavelet transform and fusion techniques were used to propose a new set of features. In addition, we introduced an effective feature selection technique, which was based on applying a filter box to find the most informative parts of the data and eliminate redundancies. These methods improved operating speeds and reduced memory usage, as shown by our empirical studies using the Signature Verification Competition 2004 (SVC04) database. We obtained a competitive Equal Error Ratio (EER) of 2.5%, with considerably fewer features. These results suggest that the proposed package is comparable with the state-of-the-art methods while using a significantly smaller number of features.