融合人脸、体型与步态，提升远距离复杂环境下的人员识别准确率。
Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait
- 统一端到端系统整合多模态生物特征，适应恶劣成像条件。
- 在BRIAR数据集上验证，1:1识别准确率提升34.1%，开放集错误率降低34.3%。
- 适用于安防监控等真实场景，尤其适合高空远距识别任务。
我们针对非受限环境下全身人员识别问题提出FarSight系统，该系统集成人脸、步态和体型三种互补生物特征，在长距离、高视角及恶劣大气条件下（如湍流、强风）实现统一建模。系统包含四个核心模块：多主体检测与跟踪、识别感知的视频恢复、模态特异性生物特征编码、质量引导的多模态融合。在最具代表性的长距离多模态生物特征识别基准BRIAR上进行大量实验表明，相比初步系统，该方法在1:1验证中实现[email protected]% FAR下34.1%的绝对精度提升，闭集识别（Rank-20）提高17.8%，开集识别错误率（FNIR@1% FPIR）降低34.3%。此外，系统通过了NIST 2025 RTE Face in Video Evaluation（FIVE），在标准测试中表现优异，确立其在复杂现实条件下操作级生物特征识别中的领先地位。
原文摘要 · Abstract (English)
We address the problem of whole-body person recognition in unconstrained environments. This problem arises in surveillance scenarios such as those in the IARPA Biometric Recognition and Identification at Altitude and Range (BRIAR) program, where biometric data is captured at long standoff distances, elevated viewing angles, and under adverse atmospheric conditions (e.g., turbulence and high wind velocity). To this end, we propose FarSight, a unified end-to-end system for person recognition that integrates complementary biometric cues across face, gait, and body shape modalities. FarSight incorporates novel algorithms across four core modules: multi-subject detection and tracking, recognition-aware video restoration, modality-specific biometric feature encoding, and quality-guided multi-modal fusion. These components are designed to work cohesively under degraded image conditions, large pose and scale variations, and cross-domain gaps. Extensive experiments on the BRIAR dataset, one of the most comprehensive benchmarks for long-range, multi-modal biometric recognition, demonstrate the effectiveness of FarSight. Compared to our preliminary system, this system achieves a 34.1% absolute gain in 1:1 verification accuracy ([email protected]% FAR), a 17.8% increase in closed-set identification (Rank-20), and a 34.3% reduction in open-set identification errors (FNIR@1% FPIR). Furthermore, FarSight was evaluated in the 2025 NIST RTE Face in Video Evaluation (FIVE), which conducts standardized face recognition testing on the BRIAR dataset. These results establish FarSight as a state-of-the-art solution for operational biometric recognition in challenging real-world conditions.
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