US army researchers have developed a convolutional neural network and a range of algorithms to recognise faces in the dark. "This technology enables matching between thermal face images and existing biometric face databases or watch lists that only contain visible face imagery," explained Benjamin Riggan on Monday, co-author of the study and an electronics engineer at the US army laboratory. "The technology provides a way for humans to visually compare visible and thermal facial imagery through thermal-to-visible face synthesis." The thermal images are processed and passed to a convolutional neural network to extract facial features using landmarks that mark the corners of the eyes, nose and lips to determine its overall shape. The system, dubbed "multi-region synthesis" is trained with a loss function so that the error between the thermal images and the visible ones is minimized, creating an accurate portrayal of what someone's face looks like despite only glimpsing it in the dark.

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. 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. 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.

We count on machine learning systems for everything from creating playlists to driving cars, but like any tool, they can be bent toward dangerous and unethical purposes, as well. Today's illustration of this fact is a new paper from Stanford researchers, who have created a machine learning system that they claim can tell from a few pictures whether a person is gay or straight. The research is as surprising as it is disconcerting. In addition to exposing an already vulnerable population to a new form of systematized abuse, it strikes directly at the egalitarian notion that we can't (and shouldn't) judge a person by their appearance, nor guess at something as private as sexual orientation from something as simple as a snapshot or two. But the accuracy of the system reported in the paper seems to leave no room for mistake: this is not only possible, it has been achieved.