Fusing Individual Algorithms and Humans Improves Face Recognition Accuracy

Recent work indicates that state-of-the-art face recognition algorithms can surpass humans matching identity in pairs of face images taken under different illumination conditions. It has been demonstrated further that fusing algorithm- and human-derived face similarity estimates cuts error rates substantially over the performance of the best algorithms. Here we employed a pattern-based classification procedure to fuse individual human subjects and algorithms with the goal of determining whether strategy differences among humans are strong enough to suggest particular man-machine combinations. The results showed that error rates for the pairwise man-machine fusions were reduced an average of 47 percent when compared to the performance of the algorithms individually. The performance of the best pairwise combinations of individual humans and algorithms was only slightly less accurate than the combination of individual humans with all seven algorithms. The balance of man and machine contributions to the pairwise fusions varied widely, indicating that a one-size-fits-all weighting of human and machine face recognition estimates is not appropriate.