Signature Verification using Geometrical Features and Artificial Neural Network Classifier
This work addresses signature verification for financial and legal organizations, but it appears incremental as it combines existing techniques without introducing a major breakthrough.
The paper tackles signature verification by proposing a method that uses geometrical features and an artificial neural network classifier, achieving a lower Equal Error Rate on the MCYT dataset and higher accuracy on the BHSig260 dataset.
Signature verification has been one of the major researched areas in the field of computer vision. Many financial and legal organizations use signature verification as access control and authentication. Signature images are not rich in texture; however, they have much vital geometrical information. Through this work, we have proposed a signature verification methodology that is simple yet effective. The technique presented in this paper harnesses the geometrical features of a signature image like center, isolated points, connected components, etc., and with the power of Artificial Neural Network (ANN) classifier, classifies the signature image based on their geometrical features. Publicly available dataset MCYT, BHSig260 (contains the image of two regional languages Bengali and Hindi) has been used in this paper to test the effectiveness of the proposed method. We have received a lower Equal Error Rate (EER) on MCYT 100 dataset and higher accuracy on the BHSig260 dataset.