Face recognition by humans: Nineteen results all computer vision researchers should know about

A key goal of computer vision researchers is to create automated face recognition systems that can equal, and eventually surpass, human performance. To this end, it is imperative that computational researchers know of the key findings from experimental studies of face recognition by humans. These findings provide insights into the nature of cues that the human visual system relies upon for achieving its impressive performance and serve as the building blocks for efforts to artificially emulate these abilities. In this paper, we present what we believe are 19 basic results, with implications for the design of computational systems. Each result is described briefly and appropriate pointers are provided to permit an in-depth study of any particular result.