Matching Depth-Rotated Faces at Varying Degrees of Physical Similarity | Zendy

Tianyi Zhu | Zendy; Miles Nelken | Zendy; Catrina Hacker | Zendy; Emily Meschke | Zendy; Irving Biederman | Zendy

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Matching Depth-Rotated Faces at Varying Degrees of Physical Similarity

Author(s) -

Tianyi Zhu,

Miles Nelken,

Catrina Hacker,

Emily Meschke,

Irving Biederman

Publication year - 2018

Publication title -

journal of vision

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.126

H-Index - 113

ISSN - 1534-7362

DOI - 10.1167/18.10.932

Subject(s) - orientation (vector space) , mathematics , similarity (geometry) , perception , matching (statistics) , artificial intelligence , face (sociological concept) , curvature , psychology , pattern recognition (psychology) , stimulus (psychology) , sample (material) , geometry , computer science , cognitive psychology , statistics , physics , image (mathematics) , social science , neuroscience , sociology , thermodynamics

4: Performance on the OFPT is significantly correlated with other measures of face recognition proficiency. Faces were presented in a triangular array (max duration = 5 s). One of the two lower (test) faces was an exact identity match to the top (sample) face, and the other was a foil (distractor) that differed metrically from the sample. Faces could be all at the same orientation, or the test faces could be rotated 13 or 20 degrees from the sample viewing angle. The same task was run with geons.

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