Standoff human identification using body shape

The ability to identify individuals is a key component of maintaining safety and security in public spaces and around critical infrastructure. Monitoring an open space is challenging because individuals must be identified and re-identified from a standoff distance non-intrusively, making methods like fingerprinting and even facial recognition impractical. We propose using body shape features as a means for identification from standoff sensing, either complementing other identifiers or as an alternative. An important challenge in monitoring open spaces is reconstructing identifying features when only a partial observation is available, because of the view-angle limitations and occlusion or subject pose changes. To address this challenge, we investigated the minimum number of features required for a high probability of correct identification, and we developed models for predicting a key body feature-height-from a limited set of observed features. We found that any set of nine randomly selected body measurements was sufficient to correctly identify an individual in a dataset of 4041 subjects. For predicting height, anthropometric measures were investigated for correlation with height. Their correlation coefficients and associated linear models were reported. These results-a sufficient number of features for identification and height prediction from a single feature- contribute to developing systems for standoff identification when views of a subject are limited.