Offline handwritten signature identification and verification using contourlet transform and Support Vector Machine

In this paper, a new method for signature identification and verification based on contourlet transform (CT) is proposed. This method uses contourlet coefficient as the feature extractor and Support Vector Machine (SVM) as the classifier. In proposed method, first signature image is normalized based on size. After preprocessing, contourlet coefficients are computed on specified scale and direction. Next, all extracted coefficients are fed to a layer of SVM classifiers as feature vector. The number of SVM classifiers is equal to the number of classes. Each SVM classifier determines if the input image belongs to the corresponding class or not. The main characteristic of proposed method is independency to nation of signers. Two experiments on two signature sets are performed. The first is on a Persian signature set and the other is on Stellenbosch (Turkish) signature set. Based on these experiments, we achieve a 100% recognition (identification) rate and more than 96.5% on Persian and Turkish signature sets respectively and 4.5% error in verification.

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