Online signature recognition: a biologically inspired feature vector splitting approach

Abstract

This research introduces an innovative approach to explore the cognitive and biologically inspired underpinnings of feature vector splitting for analyzing the significance of different attributes in e-security biometric signature recognition applications. Departing from traditional methods of concatenating features into an extended set, we employ multiple splitting strategies, aligning with cognitive principles, to preserve control over the relative importance of each feature subset. Our methodology is applied to three diverse databases (MCYT100, MCYT300, and SVC) using two classifiers (vector quantization and dynamic time warping with one and five training samples). Experimentation demonstrates that the fusion of pressure data with spatial coordinates (x and y) consistently enhances performance.