Fusion of features and classifiers for off-line handwritten signature verification

A method for writer-independent off-line handwritten signature verification based on grey level feature extraction and Real Adaboost algorithm is proposed. Firstly, both global and local features are used simultaneously. The texture information such as co-occurrence matrix and local binary pattern are analyzed and used as features. Secondly, Support Vector Machines (SVMs) and the squared Mahalanobis distance classifier are introduced. Finally, Real Adaboost algorithm is applied. Experiments on the public signature database GPDS Corpus show that our proposed method has achieved the FRR 5.64% and the FAR 5.37% which are the best so far compared with other published results.