Offline Signature Verification Using Local Features and Decision Trees

The most difficult problem of offline signature verification (SV) is that a signature is merely a static image missing a lot of the dynamic information associated with it. In this paper, three separate pseudo-dynamic features based on the gray level: gradient based local binary pattern (GLBP), statistical features of gray level co-occurrence matrix (SGLCM), simplified histogram of oriented gradients (SHOG) are proposed for writer-independent offline SV. These gray-level features can convey both texture information and the relative structural relationship of signature strokes. In addition, our experiments prove that the proposed features contain complementary information. Using random forests (RFs) as classifier, a fusion of the proposed features could achieve 7.42% and 0.08% average error rate (AER) for GPDS-253 and CEDAR datasets, respectively, which show the effectiveness of the proposed system. The implication of this paper is that part dynamic information could be extracted from a static gray level image.