Standoff human identification using body shape

Abstract

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 featurecontribute to developing systems for standoff identification when views of a subject are limited.

Reconsideration of Bertillonage in the age of digitalisation: Digital anthropometric patterns as a promising method for establishing identity

“…[ 98 , 101 , 102 ], often using Microsoft Kinect [ 37 , 97 , 100 , [103] , [104] , [105] , [106] , [107] , [108] , [109] , [110] , [111] , [112] , [113] , [114] , [115] , [116] , [117] ]for depth sensing or drawing from 3D models, Euclidean distances based on anthropometric survey data [ 118 , 119 ](e.g. CAESAR [ 23 ] [ 96 , [120] , [121] , [122] ])) or 2D as well as 3D pose estimation frameworks [ [123] , [124] , [125] , [126] , [127] , [128] , [129] , [130] , [131] , [132] , [133] , [134] ]. Notably, only few publications propose anthropometric patterns as biometric features for identification on their own [ 98 , 105 , 116 , 131 ].…”

Headgear Accessories Classification Using an Overhead Depth Sensor

“…After that, it is necessary to find the maximum height value ( ) within that contour and, finally, select the pixels whose heights are between and the minimum height of interest . Based on anthropometricconsiderations [ 26 , 27 ], we assume that: . In what follows, the set of selected pixels will be represented by .…”