Artificial Neural Network Computational Techniques in Biometric Handwriting

Abstract

This study presents a novel methodology that combines the power of multilayer perceptron (MLP) neural networks with validated graphometry approaches for individual identification based on handwriting. By integrating the computational capabilities of MLPs with the graphometry characteristics utilized in graphology, this proposal aims to leverage the distinctiveness and stability of both approaches. Handwriting, as a widely accepted behavioral biometric characteristic, serves as a reflection of an individual’s personality, enabling effective identification. The MLP’s ability to learn complex relationships between inputs and outputs, coupled with the graphometry measures capturing intricate patterns within the data, contributes to developing highly accurate and efficient identification systems. This comprehensive approach fuses the strengths of MLP neural networks and graphometry techniques, providing a promising avenue for advancing the field of personal identification through handwriting analysis. By harnessing the intrinsic uniqueness of handwriting and its equivalence to other behavioral traits, the methodology enables discerning a person’s psychological profile and overcomes variations over time. The implementation of identification systems based on these properties establishes robust and reliable solutions in personal identification.