Since 2005, human and machine performance has been systematically compared as part of face recognition competitions, with results being been reported for both still and video imagery. The key results from these competitions are reviewed. To analysis performance across studies, the cross-modal performance analysis (CMPA) framework is introduced. The CMPA framework is applied to experiment that were part of face recognition competition. The analyze 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.
Citation: Image and Vision Computing
Pub Type: Journals