: Humans are extremely powerful in recognizing faces that they see often. During encounters, we either recognize a face or reject it as unfamiliar. Praise for this ability still echoes in the literature: The only system that seems to work well under challenging conditions is the human visual system. While humans do this routinely, a particularly challenging aspect of face-recognition research is the question of rejecting previously unseen faces as unfamiliar. A system with this ability has long been desired: The similarity measure used in a face-recognition system should be designed so that humans' ability to perform face recognition and recall are imitated as closely as possible by the machine. The prevailing approach is based on matching and ranking images. Given a test image, a face-recognition algorithm finds its closest match in a database of stored images based on some similarity measure. In 'closed-world' applications, where the test image/person is guaranteed to be in the database, if the closest match is found correctly, the test image/person will be identified correctly. By contrast, in 'open-world' applications, where the test image/person may not be in the database (as might occur with watchlist surveillance, where we are interested only in recognizing 'wanted' subjects), mis-identification may occur regardless of the outcome of the search. A threshold could be used to decide whether the best match is a correct match. However, establishing the proper threshold value that works well for previously unseen data is very difficult. We have developed an approach that uses an artificial neural network to replicate the human ability to recognize faces. In contrast to most existing approaches, our approach is particularly useful for open-world applications.
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