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.