Influence of Weight Loss on 1:1 Face Verification Accuracy: Evidence Across Algorithm Generations and the Risk of Static Passport Templates

Topic: The Impact of Significant Weight Loss on the Accuracy of 1:1 Face Verification.This paper collects and compares: (1) the results of controlled studies modeling BMI/weight variations for classical face descriptors (LBP, SIFT) and correlation normalization (PLS), published in 2015; (2) more recent results (2022–2024), where geometric "face-thinning" transformations are used as a practical proxy for facial geometry changes and evaluated using modern FNMR@FMR metrics; (3) a discussion of the operational risks of centralized systems using infrequently updated passport photos (increased false rejection rates with strict thresholds); (4) a short illustrative personal case study with a successful match after ~45 kg of weight loss in a commercial 1:1 API.The case study is for demonstration purposes only and does not constitute an assessment of population-based robustness metrics.