From BoVW to VLAD with KAZE features: Offline signature verification considering cognitive processes of forensic experts

Abstract

The widespread use of handwritten signatures for identity authentication has resulted in a need for automated verification systems. However, there is room for improvement in the performance of automated systems compared to human analysis, particularly that of forensic document examiners (FDEs), under a wide range of conditions. As any inaccuracy in analysis can cause serious problems, further research is required to improve performance. In this study, to improve the performance of offline signature verification under various writing conditions, new approaches using feature encoding methods of a bag-of-visual words (BoVW) and a vector of locally aggregated descriptors (VLAD) are adopted. The proposed method considers current knowledge about the cognitive processes used by FDEs to improve performance. The proposed method incorporates an approach based on BoVW and VLAD with local features to mimic the cognitive processes of FDEs for feature extraction. In addition, KAZE features detected in strokes and background space are employed as local features to enhance discriminative power. The performance of the proposed approach is demonstrated using the publicly-available CEDAR and MCYT-75 signature datasets.