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In the last few years, several groups have announced that their facial recognition systems have achieved near-perfect accuracy rates, performing better than humans at picking the same face out of the crowd. But those tests were performed on a dataset with only 13,000 images. What happens to their performance as those crowds grow to the size of a major US city?
University of Washington researchers answered that question with the MegaFace Challenge, the world’s first competition aimed at evaluating and improving the performance of face recognition algorithms at the million-person scale. The MegaFace dataset contains 1 million images representing more than 690,000 unique people. It is the first benchmark that tests facial recognition algorithms at a million scale.Image credit: University of Washington
