Handprints and footprints as forensic anthropometric evidence for stature estimation: a systematic review

Abstract

Abstract Background Stature estimation from handprint and footprint anthropometry has gained increasing attention in forensic science, particularly in cases where conventional skeletal or fingerprint identification methods are unavailable. However, the accuracy and generalisability of existing regression models across diverse populations remains uncertain, and no comprehensive synthesis of handprint and footprint anthropometric evidence exists. This systematic review aimed to evaluate the accuracy and reliability of handprint and footprint anthropometric methods for stature estimation across different populations, identify consistent predictors of stature, and assess the degree to which regression models are transferable across ethnic groups. Main body A structured search was conducted across Web of Science, Scopus, PubMed, and Google Scholar. From 205 identified records, 61 duplicates were removed and 144 records screened. Following title and abstract screening and full-text assessment, 32 studies met predefined inclusion criteria and were included in qualitative synthesis. Methodological quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies. All 32 included studies reported statistically significant positive correlations between stature and handprint or footprint dimensions. Correlation coefficients for the strongest predictors ranged from r = 0.23 to r = 0.90. Length-based variables consistently outperformed breadth measures. Substantial variability in regression coefficients, standard errors of estimate, and optimal predictor variables was observed across populations, sexes, and measurement protocols. Conclusion Handprint and footprint anthropometry provides forensically relevant stature estimation capability across diverse populations. However, the population-specific nature of regression equations means that cross-ethnic extrapolation without validation introduces systematic bias. Methodological standardisation, sex-stratified modelling, and independent external validation are identified as primary priorities for advancing this field.