New gradient features for off-line handwritten signature verification

Abstract

This work focuses on automatic off-line handwritten signature verification where a new gradient feature is proposed for signature characterization. This feature namely, Gradient Local Binary Patterns (GLBP) takes advantage from textural information, to improve the gradient description within images. The verification step is performed in a writer-dependent framework using SVM classifier. Experimental analysis is conducted on CEDAR and GPDS-300 datasets. The results obtained in terms of average error rate highlight the high performance of the proposed feature, which significantly overcomes several state of the art results.