Offline signature verification and identification by hybrid features and Support Vector Machine

This paper emphasised an approach for offline signature verification and identification. Two image descriptors are studied, including Pyramid Histogram of Oriented Gradients (PHOG), and a direction feature proposed in the literature. Compared with many previously proposed signature feature extraction approaches, PHOG has advantages in the extraction of discriminative information from handwriting signature images. The significance of classification framework is stressed. With the benchmarking database ||Grupo de Procesado Digital de Senales|| (GPDS), satisfactory performances were obtained from several classifiers. Among the classifiers compared, SVM is clearly superior, giving a False Rejection Rate (FRR) of 2.5% and a False Acceptance Rate (FAR) 2% for skillful forgery, which compares sharply with the latest published results on the same dataset. This substantiates the superiority of the proposed method. The related issue offline signature recognition is also investigated based on the same approach, with an accuracy of 99% on the GPDS data from SVM classification.