Improvement of Japanese Signature Verification by Segmentation-Verification

This paper proposes a new combined signature verification technique called segmentation-verification based on 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 feature vector is calculated and the final decision (verification) is performed by SVM based on the three Mahalanobis distance. In the evaluation test the proposed technique achieved 94.30% verification accuracy, which is 1.03% higher than the best accuracy obtained from the full name signature image. This result shows that the proposed segmentation-verification approach improves Japanese signature verification accuracy significantly.