US Army researchers have developed an artificial intelligence and machine learning technique that holds interesting possibilities. The technology produces a visible face image from a thermal image of a person’s face captured in low-light or nighttime conditions.

This development could lead to enhanced real-time biometrics and post-mission forensic analysis for covert nighttime operations. The motivations for this technology — developed by Drs. Benjamin S. Riggan, Nathaniel J. Short and Shuowen “Sean” Hu, from the U.S. Army Research Laboratory — are to enhance both automatic and human-matching capabilities.

Thermal cameras like FLIR, or Forward Looking Infrared, sensors are actively deployed on aerial and ground vehicles, in watch towers and at check points for surveillance purposes. More recently, thermal cameras are becoming available for use as body-worn cameras.

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The ability to perform automatic face recognition at nighttime using such thermal cameras is beneficial for informing a Soldier that an individual is someone of interest, like someone who may be on a watch list.

“This technology enables matching between thermal face images and existing biometric face databases/watch lists that only contain visible face imagery,” said Riggan, a research scientist. “The technology provides a way for humans to visually compare visible and thermal facial imagery through thermal-to-visible face synthesis.”

Details of this work were presented in March in a technical paper “Thermal to Visible Synthesis of Face Images using Multiple Regions” at the IEEE Winter Conference on Applications of Computer Vision, or WACV, in Lake Tahoe, Nevada.