Bayesian Handwriting Evidence Evaluation using MANOVA via Fourier-Based Extracted Features

TL;DR

This paper introduces a Bayesian statistical framework using Fourier-based features and MANOVA models to evaluate handwriting evidence, effectively distinguishing writers by modeling character shape variability and incorporating between-writer differences.

Contribution

It presents a novel Bayesian approach with hierarchical priors and Fourier features for handwriting analysis, improving writer discrimination and model flexibility.

Findings

Bayesian MANOVA with LKJ prior outperforms other models in writer discrimination.

Fourier coefficients effectively capture character shape for statistical modeling.

Hierarchical priors enhance the model's ability to account for between-writer variability.

Abstract

This paper proposes a novel statistical approach that aims at the identification of valid and useful patterns in handwriting examination via Bayesian modeling. Starting from a sample of characters selected among 13 French native writers, an accurate loop reconstruction can be achieved through Fourier analysis. The contour shape of handwritten characters can be described by the first four pairs of Fourier coefficients and by the surface size. Six Bayesian models are considered for such handwritten features. These models arise from two likelihood structures: (a) a multivariate Normal model, and (b) a MANOVA model that accounts for character-level variability. For each likelihood, three different prior formulations are examined, resulting in distinct Bayesian models: (i) a conjugate Normal-Inverse-Wishart prior, (ii) a hierarchical Normal-Inverse-Wishart prior, and (iii) a Normal-LogNormal-LKJ prior specification. The hierarchical prior formulations are of primary interest because they can incorporate the between-writers variability, a distinguishing element that sets writers apart. These approaches do not allow calculation of the marginal likelihood in a closed-form expression. Therefore, bridge sampling is used to estimate it. The Bayes factor is estimated to compare the performance of the proposed models and to evaluate their efficiency for discriminating purposes. Bayesian MANOVA with Normal-LogNormal-LKJ prior showed an overall better performance, in terms of discriminatory capacity and model fitting. Finally, a sensitivity analysis for the elicitation of the prior distribution parameters is performed.