Sex Identification From Hand Measurements Using Machine Learning

Abstract

Sex identification plays an important role to identify individuals in a forensic investigation when unknown body parts are found. This study aims to use machine learning (ML) to determine sex using different hand dimensions in the Bangladeshi population. Nine hand measurements such as hand length, handbreadth, maximum handbreadth, palm length, thumb finger length, index finger length, middle finger length, ring finger length, and little finger length are examined to identify the sex. 292 Bangladeshi adults (145 males and 147 females) aged between 18 and 60 years old were considered as sample size, while two ML approaches such as the linear discriminant analysis and logistic regression methods are used to estimate individual sex. The ML approaches are validated using a 10-fold cross-validation scheme. Varying accuracy was found using different algorithms and different combinations of features – one feature at a time and all the features altogether. While all the features were considered altogether, for left-hand measurements, the logistic regression algorithm demonstrated maximum accuracy (91.10%), whereas for right-hand measurements altogether linear discriminant analysis algorithm showed a maximum accuracy of 91.40%. As a single feature, palm length demonstrated the maximum accuracy in both hands for all the algorithms.