Human Body Model based ID using Shape and Pose Parameters

Autor:	Sundaresan, Aravind, Burns, Brian, Sur, Indranil, Yao, Yi, Lin, Xiao, Kim, Sujeong
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: to be published in IEEE International Joint Conference on Biometrics, Ljubljana, Slovenia 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.03227 Zobrazit plný text záznamu View this record from Arxiv