Human Body Model Based ID using Shape and Pose Parameters


Aravind Sundaresan, Brian Burns, Indranil Sur, Yi Yao, Xiao Lin, Sujeong Kim; published in the IEEE International Joint Conference on Biometrics, Ljubljana, Slovenia, 2023 and arXiv:2312.03227,


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.

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