Stature Prediction from Handprint Measurements: Searching for New Parameters

Abstract

Abstract Background: Handprints are a common finding in crime scenes. Estimating stature is one of the four pillars of establishing the identity of an unknown individual. Aims: The commonly used parameters – hand length and hand breadth were tested. In addition, new parameters, namely hypothenar (HC) and thenar curvatures (TC) and palm area (PA) were examined for their usefulness in stature prediction. Subjects and Methods: A sample from the Jordanian population was used for this study. Regression analysis was employed to evaluate the accuracy of predicting stature from a handprint. Seventy-five male and female hands were scanned and processed to measure 10 parameters. Results: The results indicated that male stature and all parameters were significantly larger than their female counterparts. Regression analysis predicted the stature with a standard error of estimate of 2.09–3.90 cm in males and 5.68–3.72 cm in females. Multiple regression analysis showed a significant improvement in stature estimation. Conclusions: This study represents the first attempt to estimate stature using handprints in the Jordanian population. The newly tested parameters (HC, TC, and PA) contributed to the prediction of stature. One limitation of this study is that the research group was confined to university students aged 18–24 years.