Multi-Class Online Signature Verification Based on Hybrid Statistical Moments and UMAP-Based Nonlinear Dimensionality Reduction

Online signature verification (OSV) is a challenging problem in behavioral biometrics, especially when skilled forgeries closely mimic genuine signatures in both appearance and dynamics. This study presents a multi-class OSV framework that combines hybrid statistical features and nonlinear dimensionality reduction using Uniform Manifold Approximation and Projection (UMAP). A 40-dimensional feature set is created from statistical moments of dynamic writing parameters in both time and frequency (DCT) domains. Experimental results show that UMAP-based dimensionality reduction preserves category-related structures in a compact two-dimensional space. The proposed approach achieves an average classification accuracy of 0.989 ± 0.005 and a Cohen’s kappa coefficient of 0.985 ± 0.006, demonstrating robust performance across multiple classifiers. The results validate the effectiveness of combining multi-domain statistical feature fusion with UMAP for multi-class online signature verification, providing both high performance and interpretable visual representations.