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New AI system can recognise faces in the dark

Scientists have developed an artificial intelligence that can recognise a person's face even in the dark, a development that could lead to enhanced real-time biometrics and post-mission forensic analysis for covert nighttime operations.

Scientists have developed an artificial intelligence that can recognise a person's face even in the dark, a development that could lead to enhanced real-time biometrics and post-mission forensic analysis for covert nighttime operations.


The motivations for this technology, developed by researchers from the US Army Research Laboratory (ARL), are to enhance both automatic and human-matching capabilities.

This technology enables matching between thermal face images and existing biometric face databases/watch lists that only contain visible face imagery," said Benjamin S Riggan, a research scientist at ARL.

"The technology provides a way for humans to visually compare visible and thermal facial imagery through thermal-to-visible face synthesis," said Riggan.

Under nighttime and low-light conditions, there is insufficient light for a conventional camera to capture facial imagery for recognition without active illumination such as a flash or spotlight, which would give away the position of such surveillance cameras.

However, thermal cameras that capture the heat signature naturally emanating from living skin tissue are ideal for such conditions.

"When using thermal cameras to capture facial imagery, the main challenge is that the captured thermal image must be matched against a watch list or gallery that only contains conventional visible imagery from known persons of interest," Riggan said.

"Therefore, the problem becomes what is referred to as cross-spectrum, or heterogeneous, face recognition. In this case, facial probe imagery acquired in one modality is matched against a gallery database acquired using a different imaging modality," she said.

This approach leverages advanced domain adaptation techniques based on deep neural networks. The fundamental approach is composed of two key parts: a non-linear regression model that maps a given thermal image into a corresponding visible latent representation and an optimisation problem that projects the latent projection back into the image space.

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