GaitFace: A Multimodal Dataset for Long-Range Person Identification

Efficient border control is becoming a significant global challenge, mainly due to severe congestion and extended passenger waiting times. To mitigate these bottlenecks and facilitate passenger flow, biometric technologies are increasingly deployed to streamline identity verification and enhance crossing efficiency. Technical limitations frequently impede biometric identification, particularly in long-range surveillance, where systems must deal with adverse atmospheric conditions and degraded image quality. While high-quality frameworks like BRIAR exist, they are frequently restricted to specific government agencies. This paper introduces GaitFace, a new public dataset that contains face and gait data captured at long distances. To ensure that the research reflects authentic border scenarios, we use Pre-Enrollment data, where a traveler registers via a mobile device, and “In-the-Wild” captures, which records individuals at a distance across multiple viewing angles and different cameras. Benchmarking SOTA face and gait models reveals that current architectures fail under low-resolution and elevated viewpoints despite success with optical assistance. GaitFace exposes these critical vulnerabilities, providing a rigorous public benchmark to drive more robust, unconstrained biometric research.

GaitFace was ethically and legally acquired from 70 consenting participants across two acquisition sessions separated by several weeks, amounting to approximately 2.7 TB of multimodal data: high-resolution RGB facial images in RAW format and multi-view gait recordings, captured at distances of up to 100 meters from both a ground-level and an elevated (~9–10 m) viewpoint. Participants were recorded outdoors under natural illumination and weather variations, following four standardized scenarios: normal walking, walking while simulating a phone call, walking with an accessory (bag or backpack), and walking with a different jacket. At 100 m without optical assistance the average face resolution is 18×22 pixels (IPD 4±1.2 px), versus 210×250 pixels (IPD 55±3.5 px) with optical zoom.

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Latex citation:

@inproceedings{komaty2026gaitface,
  title={GaitFace: A Multimodal Dataset for Long-Range Person Identification},
  author={Alain Komaty and Luis Luevano and Vidit Vidit and Anjith George and Zeina Al Amine and S{\'e}bastien Marcel},
  booktitle={2026 IEEE International Joint Conference on Biometrics (IJCB)},
  year={2026},
}