Human and algorithm performance on the PaSC face Recognition Challenge

Abstract

Face recognition by machines has improved substantially in the past decade and now is at a level that compares favorably with humans for frontal faces acquired by digital single lens reflex cameras. We expand the comparison between humans and algorithms to still images and videos taken with digital point and shoot cameras. The data used for this comparison are from the Point and Shoot Face Recognition Challenge (PaSC). For videos, human performance was compared with the four top performers in the Face and Gesture 2015 Person Recognition Evaluation. In the literature, there are two methods for computing human performance: aggregation and fusion. We show that the fusion method produces higher performance estimates. We report performance for two levels of difficulty: challenging and extremely-difficult. Our results provide additional evidence that human performance shines relative to algorithms on extremely-difficult comparisons. To improve the community's understanding of the state of human and algorithm performance, we update the cross-modal performance analysis in Phillips and O'Toole [22] with these new results.