“An engineering team has developed a face recognition system that is remarkably accurate in realistic situations. Unlike existing face recognition programs that try to find ‘optimal’ facial features, the new program uses sparse representation. One of the program’s developers, Yi Ma, an associate professor at the University of Illinois, contends that the choice of features is less important than the number of features used. ‘Face recognition is not new, but new mathematical models have allowed researchers to identify faces so occluded that it was previously thought impossible,’ says Ma. People can learn upwards of tens of thousands of different human faces during their lifetime. Various real-world situations such as lighting, background, pose, expression, and occlusion may complicate human recognition, but are incredibly difficult problems for traditional face recognition algorithms to conquer. Ma’s sparse representation algorithm randomly selects pixels from all over the face, increasing the accuracy of recognition even in cases of disguise, varying expressions, or poor image quality.”
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