Scalable spatial point process models for forensic footwear analysis

2026-04-20 · Source: stat.ML updates on arXiv.org · Field: Science & Research — Mathematics & Computational Sciences, Research Methodology & Innovation · Depth: Expert, extended

Summary

This study introduces a hierarchical Bayesian model for scalable spatial point process analysis in forensic footwear, specifically for quantifying the rarity of "accidentals" (cuts, scrapes) on shoe soles. The model improves upon existing methods by framing the approach as a latent Gaussian model, enabling efficient inference on large datasets via integrated nested Laplace approximations (INLA). It also incorporates spatially varying coefficients to model the relationship between shoe tread patterns and accidental locations. The research utilizes the West Virginia University (WVU) database, comprising 1,300 distinct shoes, each with a laboratory-generated impression and annotated accidental coordinates. This new model demonstrates superior performance on held-out data, enhancing the accuracy and reliability of forensic shoe print analysis by providing data-driven probability estimates for accidental patterns.

Key takeaway

For AI Scientists developing forensic analysis tools, this research highlights a robust, scalable Bayesian approach for quantifying shoe accidental rarity. You should consider implementing latent Gaussian models with INLA for high-dimensional spatial data, as it significantly improves accuracy and computational efficiency over traditional methods. This framework allows for more reliable probability estimates in forensic footwear evidence, moving beyond subjective expert testimony.

Key insights

A new Bayesian model uses latent Gaussian processes and INLA for scalable, accurate forensic footwear accidental analysis.

Principles

• Quantify accidental rarity for forensic evidence strength.
• Model accidental locations as spatial Poisson point processes.
• Use contact surface as a proxy for class characteristics and wear.

Method

The method frames accidental configurations as a latent Gaussian spatial point process model, enabling fast approximate Bayesian inference via integrated nested Laplace approximations (INLA). It incorporates spatially varying coefficients for outsole contact-surface features.

In practice

• Apply INLA for efficient Bayesian inference on large spatial datasets.
• Utilize spatially varying coefficients to capture non-linear feature relationships.
• Process shoe print images to a 39x91 resolution for modeling.

Topics

• Forensic Footwear Analysis
• Spatial Point Process Models
• Latent Gaussian Models
• INLA Inference
• Shoe Print Accidentals

Best for: AI Scientist, Research Scientist, Data Scientist

Related on AIssential

• Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation — SCKD distills knowledge from large models to create efficient, accurate GRF estimation models for wearable sensors.
• Corrected Integrated Laplace Approximation for Bayesian Inference in Latent Gaussian Models — Importance sampling corrects Integrated Laplace Approximation errors for asymptotically exact Bayesian posteriors in LGMs.
• DRIFT: From Robustness Gaps to Invariance Manifolds for AI-Generated Image Detection — AI-generated image detection improves by learning a structured invariance manifold of real images using robust and fragile representation subspaces.
• Mind the Unseen Mass: Unmasking LLM Hallucinations via Soft-Hybrid Alphabet Estimation — SHADE improves LLM uncertainty quantification by adaptively fusing missing-mass extrapolation and spectral graph analysis, especially with few samples.
• Self-Supervised Learning with Gaussian Processes — GPSSL uses Gaussian processes to learn data representations, offering inherent similarity grouping and uncertainty quantification.
• Bayesian Poisson-Randomized Gamma Tensor Factorization with Application to International Trade Flows — A Bayesian Poisson-randomized Gamma tensor factorization models sparse, semi-continuous multiway data with excess zeros and heterogeneous dispersion.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.