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We present a synopsis of results comparing the performance of humans with face recognition algorithms tested in the face recognition vendor test (FRVT) 2006 and face recognition grand challenge (FRGC). Algorithms and humans matched face identity in images taken under controlled and uncontrolled illumination. The human-machine comparisons include accuracy benchmarks, an error pattern analysis, and a test of human and machine performance stability across data sets varying in image quality. The results indicate that: (1.) machines can compete quantitatively with humans matching face identity across changes in illumination; (2.) qualitative differences between humans and machines can be exploited to improve identification by fusing human and machine match scores; and (3.) recognition skills for humans and machines are comparably stable across changes in image quality. Combined the results suggest that face recognition algorithms may be ready for applications with task constraints similar to those evaluated in the FRVT 2006.