Signature recognition by using SIFT and SURF with SVM basic on RBF for voting online

The Signature recognition is known as the process to verify a writer by examining the signature upon samples has been studied and stored in the database. This process has two types: The offline and the online. This paper deals with the offline technique. This paper proposed a SIFT and a SURF algorithm which is used for enhanced offline signature recognition. This process, Bag-of-word features, was operated by making vector quantization technique, which outlined the key points for each training image inside a unified dimensional histogram. We put features of bag-of-word inside multiclass Support Vector Machine (SVM) classifier established upon the radial basis function (RBF) for a training and testing. We used Open CV C++ as an image processing tool and tool for feature extraction. In this paper, we compare the performance of SIFT on SVM based RBF kernel with SURF on SVM based RBF kernel. It was found out that the use of SIFT with SVM-RBF kernel system, it has an accuracy of 98.75% compared that of SURF with SVM-RBF kernel it has an accuracy of 96.25%.