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FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude
Conference proceeding   Open access

FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude

Feng Liu, Ryan Ashbaugh, Nicholas Chimitt, Najmul Hassan, Ali Hassani, Ajay Jaiswal, Minchul Kim, Zhiyuan Mao, Christopher Perry, Zhiyuan Ren, …
2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp 6215-6224
03 Jan 2024
DOI: https://doi.org/10.1109/WACV57701.2024.00611
url
http://arxiv.org/abs/2306.17206View
SubmittedarXiv.org - Non-exclusive license to distribute,  Open

Abstract

Biological system modeling Biometrics body pose Deep learning face Face recognition gesture Imaging Surveillance Training US Department of Homeland Security Algorithms
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 +11.30% TAR@1% FAR.

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