Abstract

Background: On regular basis, crimes are committed and the perpetrators of such offences roam the streets unidentified because of insufficient evidence to connect the suspect to the crime. Therefore handprints hold a cardinal role in linking offenders to crimes and its correlation to stature cannot be undermined as it widens the prospect and precision of human identification in medico-legal investigations

Objective: The objective of the current study is to derive regression models that will predict stature from hand prints parameters amongst Nigerian adults.

Subjects and method This cross-sectional research comprises of a total sample size of 230(100 males and 130 females) healthy adult Nigerians, aged between 18 to 36 years. This study employed direct and indirect method to acquire handprints dimensions (Handprint Length, Breadth, palm print length and digit length of left and right hand) following standard procedures. The data derived were subjected to series of statistical analysis using Statistical Package for Social Sciences (SPSS version 20 Chicago Inc) including descriptive statistics, independent and paired sample t-test, Pearson moment correlation coefficient and Durbin Watson regression.

Results: The present results for stature records 176.36±8.13cm and 164.38±6.62 for males and females sample respectively. Values of handprints dimension showed a range of positive Pearson moment correlation coefficient (r) of 0.31 to 0.73 which represent weak to strong r value. The regressions formulas derive were observed to be more reliable in multiple linear regression equations when reconstructing stature than single linear equation due to different levels of standard error of estimates (SEE) and coefficient of determination (R2) using>99% accurate estimation rate of the equations.

Conclusion: The regression models derived can effectively reconstruct stature which may be useful to a forensic expert saddled with the task of human identification among disaster victims or crime scene.

Keywords: Forensic sciences; Handprints; Stature reconstruction; Human identification; Adult Nigerians