Review ArticleEditor's Choice Article: Comparison of human and computer performance across face recognition experiments

Authors:
P. Jonathon Phillips;Alice J. O'toole
Affiliations:
National Institute of Standards and Technology, 100 Bureau Drive MS 8490, Gaithersburg, MD 20899, USA;University of Texas at Dallas, School of Behavioral and Brain Sciences, GR4.1, Richardson, TX 75083-0688, USA
Venue:
Image and Vision Computing
Year:
2014

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Abstract

Since 2005, human and computer performance has been systematically compared as part of face recognition competitions, with results being reported for both still and video imagery. The key results from these competitions are reviewed. To analyze performance across studies, the cross-modal performance analysis (CMPA) framework is introduced. The CMPA framework is applied to experiments that were part of face a recognition competition. The analysis shows that for matching frontal faces in still images, algorithms are consistently superior to humans. For video and difficult still face pairs, humans are superior. Finally, based on the CMPA framework and a face performance index, we outline a challenge problem for developing algorithms that are superior to humans for the general face recognition problem.