Improvement of Japanese Signature Verification by Combined Segmentation Verification Approach

This paper proposes a new signature verification technique called combined segmentation-verification based on off-line features and on-line features. We use three different off-line feature vectors extracted from full name Japanese signature image and from the sub-images of the first name and the last name. The Mahalanobis distance for each offline feature vector is calculated for signature verification. The on-line feature based technique employs dynamic programming (DP) matching technique for time series data of the signatures. The final decision (verification) is performed by SVM based on the three Mahalanobis distances and the dissimilarity of the DP matching. In the evaluation test the proposed technique achieved 97.22% verification accuracy with even FRR and FAR, which is 3.95% higher than the best accuracy obtained by the individual technique. This result shows that the proposed combined segmentation verification approach improves Japanese signature verification accuracy significantly.