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.

我们提出了一种基于人体模型的身份识别系统 (HMID) 系统，该系统联合训练了形状、姿势和生物特征识别。该 HMID 模型在使用额外的形状和姿势损失进行训练时，在生物特征识别性能方面表现出显著改进，且在衣物、视角和范围变化时仍能进行稳健的图像识别。