Role of featural and configurai information in familiar and unfamiliar face recognition

Abstract

Using psychophysics we investigated to what extent human face recognition relies on local information in parts (featural information) and on their spatial relations (configurai information). This is particularly relevant for biologically motivated computer vision since recent approaches have started considering such featural information. In Experiment 1 we showed that previously learnt faces could be recognized by human subjects when they were scrambled into constituent parts. This result clearly indicates a role of featural information. Then we determined the blur level that made the scrambled part versions impossible to recognize. This blur level was applied to whole faces in order to create configurai versions that by definition do not contain featural information. We showed that configurai versions of previously learnt faces could be recognized reliably. In Experiment 2 we replicated these results for familiar face recognition. Both Experiments provide evidence in favor of the view that recognition of familiar and unfamiliar faces relies on featural and configurai information. Furthermore, the balance between the two does not differ for familiar and unfamiliar faces. We propose an integrative model of familiar and unfamiliar face recognition and discuss implications for biologically motivated computer vision algorithms for face recognition.