Predicting Human Performance for Face Recognition1 Alice J. O’Toole Fang Jiang The University of Texas at Dallas The University of Texas at Dallas Email:
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[email protected] Dana Roark Hervé Abdi The University of Texas at Dallas The University of Texas at Dallas Email:
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[email protected] Abstract The ability of humans to recognize faces provides an implicit benchmark for gauging the performance of automatic face recognition algorithms. In this chapter we review the factors that affect human accuracy. These factors can be classified into facial stucture constraints and viewing parameters. The former include factors such as face typicality, gender, and ethnicity. The latter include changes in illumination and viewpoint, as well as the perceptual complications introduced when we see faces and people in motion. The common thread of the chapter is that human experience and familiarity with faces can overcome many, if not all, of these challenges to face recognition. A goal of computional algorithms should be to emulate the ways in which humans acquire familiarity with faces. It may then be possible to apply these principles to the design of algorithms to meet the pressing challenges of face recognition in naturalistic vieiwng conditions. 9.1 Introduction Human face recognition abilities are impressive by comparison to the performance of many current automatic face recognition systems. Such is the belief of many psy- chologists and computer scientists. This belief, however, is supported more by anec- dotal impression than by scientific evidence. In fact, there are relatively few system- atic and direct comparisons between the performance of humans and automatic face recognition algorithms, (though see [84], for exceptions).