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. However, the inclusion of pen-tip angles in the same feature set yields mixed results, with performance improvements observed in select cases. This work delves into the cognitive aspects of feature fusion, shedding light on the cognitive relevance of feature vector splitting in e-security biometric applications.

Application of nonparametric quantifiers for online handwritten signature verification: A statistical learning approach

“…from prior studies discussing the implementation of biometric systems and machine learning methods [5,19,60,[78][79][80][81][82]. Signatures are the input and they are first scaled to fit a unitary square, and interpolated in order to have the same number of data for all subjects.…”