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Learning Faces from Multiple Viewpoints Eliminates the Other-Race Effect

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Zhao,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Buelthoff,  I
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Zhao, M., & Buelthoff, I. (2013). Learning Faces from Multiple Viewpoints Eliminates the Other-Race Effect. Poster presented at 36th European Conference on Visual Perception (ECVP 2013), Bremen, Germany.


Cite as: https://hdl.handle.net/21.11116/0000-0001-4E63-9
Abstract
People recognize own-race faces more accurate than those of other races. This other-race effect (ORE) has been frequently observed when faces are learned from static, single view images. However, the single-view face learning may prevent the acquisition of useful information (e.g., 3D face shape) for recognizing unfamiliar, other-race faces. Here we tested whether learning faces from multiple viewpoints reduces the ORE. In Experiment 1 participants learned faces from a single viewpoint (left or right 15° view) and were tested with front view(0° view) using an old/new recognition task. They showed better recognition performances for own-race faces than that for other-race faces, demonstrating the ORE in face recognition across viewpoints. In Experiment 2 participants learned each face from four viewpoints (in order, left 45°, left 15°, right 15°, and right 45° views) and were tested in the same way as in Experiment 1. Participants recognized own- and other-race faces equally well, eliminating the ORE. These results suggest that learning faces from multiple viewpoints improves the recognition of other-race faces more than that for own-race faces, and that previously observed ORE is caused in part by the non-optimal encoding condition for other-race faces.