Advanced LiDAR System Could Enable Long-Range Facial Recognition, Biometric Detection

Researchers have developed a LiDAR system capable of capturing high-resolution 3D facial features and detecting human activity at distances up to one kilometer, even through environmental obstacles like fog and smoke. The collaborative effort between Heriot-Watt University, NASA’s Jet Propulsion Laboratory, MIT, and the University of Glasgow advances biometric detection capabilities.A hovering drone device equipped with a camera, floating in an urban environment.

The system’s core innovation is its superconducting nanowire single-photon detector (SNSPD), which achieves timing precision of 13 picoseconds in measuring laser pulse travel times. Operating at a 1550-nanometer wavelength, the eye-safe system delivers millimeter-scale depth resolution while minimizing solar interference.

During testing, researchers demonstrated the system’s ability to capture detailed facial features and human activities at distances of 45 meters, 325 meters, and up to one kilometer. The technology performs effectively in both daylight and darkness, and can penetrate atmospheric obscurants.

The development coincides with increased adoption of facial recognition technology in security applications. This new LiDAR technology could address existing constraints, particularly in long-range detection and environmental resilience.

The advancement aligns with industry trends, as previous research has indicated growing applications for LiDAR in smart cities and security sectors. The technology could enhance perimeter surveillance, threat assessment, and search and rescue operations.

The system may interest defense and security organizations. The U.S. Department of Defense’s Office of Strategic Capital, for instance, funds national security technology development, and could potentially support further advancement of such capabilities.

Researchers acknowledge the need for continued development to optimize scanning speed and reduce system complexity for commercial deployment. Future work will focus on integrating machine learning algorithms to enhance image processing and real-time decision-making capabilities.

Sources: Heriot Watt University, Optica

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February 7, 2025 – by Cass Kennedy