FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude
Abstract
Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance. This paper presents the end-to-end design, development and evaluation of FarSight, an innovative software system designed for whole-body (fusion of face, gait and body shape) biometric recognition. FarSight accepts videos from elevated platforms and drones as input and outputs a candidate list of identities from a gallery. The system is designed to address several challenges, including (i) low-quality imagery, (ii) large yaw and pitch angles, (iii) robust feature extraction to accommodate large intra-person variabilities and large inter-person similarities, and (iv) the large domain gap between training and test sets. FarSight combines the physics of imaging and deep learning models to enhance image restoration and biometric feature encoding. We test FarSight's effectiveness using the newly acquired IARPA Biometric Recognition and Identification at Altitude and Range (BRIAR) dataset. Notably, FarSight demonstrated a substantial performance increase on the BRIAR dataset, with gains of +11.82% Rank-20 identification and +11.3% TAR@1% FAR.
Keywords
Cite
@article{arxiv.2306.17206,
title = {FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude},
author = {Feng Liu and Ryan Ashbaugh and Nicholas Chimitt and Najmul Hassan and Ali Hassani and Ajay Jaiswal and Minchul Kim and Zhiyuan Mao and Christopher Perry and Zhiyuan Ren and Yiyang Su and Pegah Varghaei and Kai Wang and Xingguang Zhang and Stanley Chan and Arun Ross and Humphrey Shi and Zhangyang Wang and Anil Jain and Xiaoming Liu},
journal= {arXiv preprint arXiv:2306.17206},
year = {2023}
}
Comments
11 pages, 7 figures, accepted in WACV 2024