Abstract An online signature verification algorithm is presented based on feature extraction and feature selection. A novel digital tablet, called F-Tablet, is used to capture the signature information. The tablet can capture both shape series and five-dimensional forces. Total 188 features are extracted from each signature and then divided into three classes. Then, the weight function of features F is defined and the 188 features are sorted according to the F values. With different thresholds, different feature sets are obtained. The SVM is used to train the selected feature sets in the training process and the signatures are verified by the trained models. The proposed algorithm achieves false rejection rate (FRR) of 1.2% and false acceptance rate (FAR) of 3.7%.
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2009, Vol. 22 
