Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Object Classification Using a Fragment-Based Representation
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
Categorization of natural scenes: local vs. global information
APGV '06 Proceedings of the 3rd symposium on Applied perception in graphics and visualization
Region-based representations for face recognition
ACM Transactions on Applied Perception (TAP)
Proceedings of the 4th symposium on Applied perception in graphics and visualization
Categorization of natural scenes: Local versus global information and the role of color
ACM Transactions on Applied Perception (TAP)
Modelling human perception of static facial expressions
Image and Vision Computing
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Using psychophysics we investigated to what extent human face recognition relies on local information in parts (featural information) and on their spatial relations (configural 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 configural versions that by definition do not contain featural information. We showed that configural 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 configural 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.