Classifier performance evaluation for offline signature verification using local binary patterns

Handwritten signatures are one of the most widely used biometrics, particularly in financial and legal transactions. Offline Signature verification is still one of the most challenging problems in biometrics. In this study, we have evaluated the performance of different classifiers for offline signature verification based upon local binary patterns feature set. The feature vector is formed by dividing the signature images into twelve local regions and forming a code matrix by their LBPs. The histogram of each code matrix is formulated and concatenated. The dimensionality of feature vector is subsequently reduced by keeping the 256 DCT coefficients of the concatenated vector. We have investigated the performance of seven classifiers on FUM-PHSDB dataset comprising 20 classes of genuine and forged signatures of depth 20 and 10 respectively. Experimental findings depict that LS-SVM performs the best among the seven classifiers, achieving the Equal Error Rate (EER) of 13%.