Linear models for the prediction of stature from foot and boot dimensions

Estimation of stature from the dimensions of foot or shoeprints has considerable forensic value in developing descriptions of suspects from evidence at the crime scene and in corroborating height estimates from witnesses. This study extends the findings of previous researchers by exploring linear models with and without gender and race indicators, and by validating the most promising models on a large, recently collected military database. Boot size and outsole dimensions are also examined as predictors of stature. The results of this study indicate that models containing both foot length and foot breadth are significantly better than those containing only foot length. Models with race/gender indicators also perform significantly better than do models without race/gender indicators. However, the difference in performance is slight, and the availability of reliable gender and race information in most forensic situations is uncertain. Analogous results were obtained for models utilizing boot size/width and outsole length/width, and in this study these variables performed nearly as well as the foot dimensions themselves. Although the adjusted R2 values for these models clearly reflect a strong relationship between foot/boot length and stature, individual 95% prediction limits for even the best models are +/- 86 mm (3.4 in.). This suggests that models estimating stature from foot/shoe-prints may be useful in the development of subject descriptions early in a case but, because of their imprecision, may not always be helpful in excluding individual suspects from consideration.