A Quantitative Method for Shoulder Presentation Evaluation in Biometric Identity Documents

TL;DR

This paper introduces a quantitative algorithm for assessing shoulder presentation in biometric ID photos, using 3D pose data to ensure compliance with international standards.

Contribution

It presents a novel SPE algorithm that quantifies shoulder yaw and roll from 3D landmarks, filling a gap in automated biometric quality assessment.

Findings

Strong correlation (r approx. 0.80) with human labels

Effective in filtering non-compliant samples

Lightweight and suitable for automated systems

Abstract

International standards for biometric identity documents mandate strict compliance with pose requirements, including the square presentation of a subject's shoulders. However, the literature on automated quality assessment offers few quantitative methods for evaluating this specific attribute. This paper proposes a Shoulder Presentation Evaluation (SPE) algorithm to address this gap. The method quantifies shoulder yaw and roll using only the 3D coordinates of two shoulder landmarks provided by common pose estimation frameworks. The algorithm was evaluated on a dataset of 121 portrait images. The resulting SPE scores demonstrated a strong Pearson correlation (r approx. 0.80) with human-assigned labels. An analysis of the metric's filtering performance, using an adapted Error-versus-Discard methodology, confirmed its utility in identifying non-compliant samples. The proposed algorithm is a viable lightweight tool for automated compliance checking in enrolment systems.