The Writer-Independent Approach Based On the Combination of Parametric Features and Local Binary Pattern in the Offline Signature Recognition System

Today, it is very important to authentication and identification in different organizations; therefore, the presentation of authentication systems with the ability to identify individuals and matching the input pattern has received much attention. The offline signature verification system is one of the biometric subsets of behavior used to verify the identity of the claimant. One of the major challenges in the signature feature is the reduction of intra-class diversity among the genuine and forged samples. For this purpose, in this paper, in order to increase the performance of the system, the combination of parametric features such as extraction of the feature in the radius of the intersecting points and the local binary pattern is discussed. In the proposed method, considering the spatial distribution of connected pixels, the neighbors of the candidate points are examined; therefore, by having local details about the candidate points, the strokes, arcs and angles of the signature pixels are extracted. The experiments used standard MCYT, GPDSsynthetic and CEDAR databases. The samples were separated using the KNN binder and based on the writer independent structure. According to the results, the average error rate in each database is 0.036, 0.033 and 0.12, respectively. In addition, the results of sensitivity and specificity criteria have improved compared to previous work.