Journal of Pattern Recognition Research

Static signature verification has a significant use in establishing the authenticity of bank checks, insurance and legal documents based on the signatures they carry. As an individual signs only a few times on the forms for opening an account with any bank or for insurance related purposes, the number of genuine signature templates available in banking and insurance applications is limited, a new approach of static handwritten signature verification based on Dynamic Time Warping (DTW) by using only five genuine signatures for training is proposed in this paper. Initially the genuine and test signatures belonging to an individual are normalized after calculating the aspect ratios of the genuine signatures. The horizontal and vertical projection features of a signature are extracted using discrete Radon Transform and the two vectors are combined to form a combined projection feature vector. The feature vectors of two signatures are matched using DTW algorithm. The closed area formed by the matching path around the diagonal of the DTW-grid is computed and is multiplied with the difference cost between the feature vectors. A threshold is calculated for each genuine sample during the training. The test signature is compared with each genuine sample and a matching score is calculated. A decision to accept or reject is made on the average of such scores. The entire experimentations were performed on a global signature database (GPDS-Signature Database) of 2106 signatures with 936 genuine signatures and 1170 skilled forgeries. Experiments were carried out with 4 to 5 genuine samples for training and with different ‘scores’. The proposed as well as the existing DTW-method were implemented and compared. It is observed that the proposed method is superior in terms of Equal Error Rate (EER) and Total Error Rate (TER) when 4 or 5 genuine signatures were used for training. Also it is observed that the False Acceptance Rate (FAR) of the proposed system decreases as the number of genuine training samples increases.