Long-Range Biometric Identification in Real World Scenarios: A Comprehensive Evaluation Framework Based on Missions
<|reference_start|>Long-Range Biometric Identification in Real World Scenarios: A Comprehensive Evaluation Framework Based on Missions: The considerable body of data available for evaluating biometric recognition systems in Research and Development (R\&D) environments has contributed to the increasingly common problem of target performance mismatch. Biometric algorithms are frequently tested against data that may not reflect the real world applications they target. From a Testing and Evaluation (T\&E) standpoint, this domain mismatch causes difficulty assessing when improvements in State-of-the-Art (SOTA) research actually translate to improved applied outcomes. This problem can be addressed with thoughtful preparation of data and experimental methods to reflect specific use-cases and scenarios.   To that end, this paper evaluates research solutions for identifying individuals at ranges and altitudes, which could support various application areas such as counterterrorism, protection of critical infrastructure facilities, military force protection, and border security. We address challenges including image quality issues and reliance on face recognition as the sole biometric modality. By fusing face and body features, we propose developing robust biometric systems for effective long-range identification from both the ground and steep pitch angles. Preliminary results show promising progress in whole-body recognition. This paper presents these early findings and discusses potential future directions for advancing long-range biometric identification systems based on mission-driven metrics.<|reference_end|>
@article{aykac2024long-range,
    title={Long-Range Biometric Identification in Real World Scenarios: A
  Comprehensive Evaluation Framework Based on Missions},
    author={Deniz Aykac, Joel Brogan, Nell Barber, Ryan Shivers, Bob Zhang, Dallas
  Sacca, Ryan Tipton, Gavin Jager, Austin Garret, Matthew Love, Jim Goddard,
  David Cornett III and David S. Bolme},
    journal={arXiv preprint arXiv:2409.01540},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01540},
    primaryClass={cs.CV cs.AI cs.LG}
}