Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2025
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| _version_ | 1866910931612598272 |
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| author | Liu, Feng Chimitt, Nicholas Guo, Lanqing Jain, Jitesh Kane, Aditya Kim, Minchul Robbins, Wes Su, Yiyang Ye, Dingqiang Zhang, Xingguang Zhu, Jie Satyakam, Siddharth Perry, Christopher Chan, Stanley H. Ross, Arun Shi, Humphrey Wang, Zhangyang Jain, Anil Liu, Xiaoming |
| author_facet | Liu, Feng Chimitt, Nicholas Guo, Lanqing Jain, Jitesh Kane, Aditya Kim, Minchul Robbins, Wes Su, Yiyang Ye, Dingqiang Zhang, Xingguang Zhu, Jie Satyakam, Siddharth Perry, Christopher Chan, Stanley H. Ross, Arun Shi, Humphrey Wang, Zhangyang Jain, Anil Liu, Xiaoming |
| contents | 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 (TAR@0.1% 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04616 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait Liu, Feng Chimitt, Nicholas Guo, Lanqing Jain, Jitesh Kane, Aditya Kim, Minchul Robbins, Wes Su, Yiyang Ye, Dingqiang Zhang, Xingguang Zhu, Jie Satyakam, Siddharth Perry, Christopher Chan, Stanley H. Ross, Arun Shi, Humphrey Wang, Zhangyang Jain, Anil Liu, Xiaoming Computer Vision and Pattern Recognition 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 (TAR@0.1% 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. |
| title | Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.04616 |