An improved Artificial Immune Recognition System for off-line handwritten signature verification

This paper introduces an improved implementation of Artificial Immune Recognition System (AIRS) to solve the automatic off-line handwritten signature verification. Conventionally, the AIRS training provide a set of memory cells that are used with a k-Nearest Neighbors decision to classify test patterns. In order to improve the verification ability, we propose to substitute the k-NN classification by a trainable decision function using SVM classifier. In addition, for signature characterization, new gradient local binary pattern features are introduced. Experiments are conducted on CEDAR and GPDS-300 corpuses. The results show that the proposed algorithm overcomes the conventional AIRS-kNN by more than 9% in the average error rate. Also, it gives similar and sometimes better performance than the state of the art.