Human Body Model based ID using Shape and Pose Parameters
Aravind Sundaresan Brian Burns Indranil Sur Yi Yao Xiao Lin Sujeong Kim
We present a Human Body model based IDentification system (HMID) system that
is jointly trained for shape, pose and biometric identification. HMID is based
on the Human Mesh Recovery (HMR) network and we propose additional losses to
improve and stabilize shape estimation and biometric identification while
maintaining the pose and shape output. We show that when our HMID network is
trained using additional shape and pose losses, it shows a significant
improvement in biometric identification performance when compared to an
identical model that does not use such losses. The HMID model uses raw images
instead of silhouettes and is able to perform robust recognition on images
collected at range and altitude as many anthropometric properties are
reasonably invariant to clothing, view and range. We show results on the USF
dataset as well as the BRIAR dataset which includes probes with both clothing
and view changes. Our approach (using body model losses) shows a significant
improvement in Rank20 accuracy and True Accuracy Rate on the BRIAR evaluation
dataset.