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It\u2019s no mean feat for a computer\u00a0<\/strong>to identify an individual\u2019s face in daylight. The process involves precisely measuring a photograph \u2014 eye size, distance from nose to mouth, etc.\u00a0\u2014 adjusting the distances for three dimensions, and searching a database for a match. But to do it at night, when all you have is far lower-resolution thermal images, the Army Research Lab used a technique that allows software to mimic the human\u00a0brain.<\/p>\nOur brains \u201csee\u201d by extrapolating a picture from a relatively small amount of sensory data, filtered through the eye. The\u00a0brain uses several times more neuronal mass<\/a><\/b>\u00a0to construct images from visual data than the eye does collecting the\u00a0data.<\/p>\n The Army researchers saw a parallel with thermal images. Such images show what parts of the face are hotter and cooler, but generally contain fewer data points than a comparable optical image from a camera, making it hard to pick out distinct features. So they set up a convolutional neural network, or\u00a0CNN, a deep-learning method that uses specific nodes similar to the brain\u2019s, and set it to infer faces from limited\u00a0data.<\/p>\n The method that the researchers use breaks a thermal picture of a face into specific regions and then compares them to an optical image of the same face. The network estimates where key features are in the thermal image in relation to the conventional image. The network\u2019s final product is something like a police sketch \u2014 not a perfect match, but with enough overlap in key points to make a high-certainty\u00a0match.<\/p>\n