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.

翻译：全身生物特征识别因其在执法、边境安防和监控等领域的广泛应用而成为重要研究方向。本文展示了 FarSight 的端到端设计、开发与评估——这是一款面向全身生物特征识别（融合人脸、步态与体形特征）的创新软件系统。FarSight 接收来自高空平台和无人机视频作为输入，输出候选身份列表。该系统旨在解决多项挑战，包括：(i) 低质量图像；(ii) 大偏航角和俯仰角；(iii) 鲁棒特征提取以应对显著的类内差异与类间相似性；(iv) 训练集与测试集之间的巨大领域差异。FarSight 结合成像物理学与深度学习模型，增强图像复原与生物特征编码。我们使用新采集的 IARPA 高海拔远距离生物特征识别与确认（BRIAR）数据集验证 FarSight 的有效性。实验表明，FarSight 在 BRIAR 数据集上展现出显著性能提升：Rank-20 识别率提高 +11.82%，TAR@1% FAR 提高 +11.3%。