  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.
