Predictive ability of anthropomorphic metrics in determining age and sex of children

Capturing age and sex of subjects from whole‐body features has important applications in a wide variety of areas. However, current techniques for determining this information without subject interaction or high‐resolution images are problematic. While computer vision techniques (e.g. poselets and histogram‐oriented gradients) are functional at a stand‐off, these methods often include areas influenced by characteristics such as clothing or hairstyle which vary by region and culture. Whole‐body anthropometrics, especially those of children and youth experiencing rapid musculoskeletal changes, may help inform robust models of age estimation and sex classification. Models of anthropometric variables were developed from a pre‐existing database for age estimation using linear regression techniques. Sex classification was performed both over the entire subject group as well as three individual age bins (2 ≤ subject age < 8, 8 ≤ subject age < 14, and 14 ≤ subject age). Age estimation models were highly dependent on head size and exhibited r ‐squared values as high as 0.91 and root mean square error values as low as 1.29 years. Sex classification was found to be highly linked to a combination of foot, hand, hip, and torso metrics for correct classification as high as 88%. The results presented herein may help develop and focus methods of determining age and sex.