There has been significant progress in improving the performance of computer-based face recognition algorithms over the last decade. Although algorithms have been tested and compared extensively with each other, there has been remarkably little work comparing the accuracy of computer-based face recognition systems with humans. We compared seven state-of-the-art face recognition algorithms with humans on a facematching task. Humans and algorithms determined whether pairs of face images, taken under different illumination conditions, were pictures of the same person or of different people. Three algorithms surpassed human performance matching face pairs prescreened to be "difficult" and six algorithms surpassed humans on "easy" face pairs. Although illumination variation continues to challenge face recognition algorithms, current algorithms compete favorably with humans. The superior performance of the best algorithms over humans, in light of the absolute performance levels of the algorithms, underscores the need to compare algorithms with the best current control--humans.

We describe a database of static images and video clips of human faces and people that is useful for testing algorithms for face and person recognition, head/eye tracking, and computer graphics modeling of natural human motions. For each person there are nine static "facial mug shots" and a series of video streams. The videos include a "moving facial mug shot," a facial speech clip, one or more dynamic facial expression clips, two gait videos, and a conversation video taken at a moderate distance from the camera. Complete data sets are available for 284 subjects and duplicate data sets, taken subsequent to the original set, are available for 229 subjects.