August 2014
Volume 14, Issue 10
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2014
Why the Long Face? The critical role of vertical configural relations in face 'barcodes' for recognition
Author Affiliations
  • Morgan Spence
    Perception Lab, School of Psychology, The University of Queensland
  • Katherine Storrs
    Perception Lab, School of Psychology, The University of Queensland
  • Derek Arnold
    Perception Lab, School of Psychology, The University of Queensland
Journal of Vision August 2014, Vol.14, 574. doi:10.1167/14.10.574
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      Morgan Spence, Katherine Storrs, Derek Arnold; Why the Long Face? The critical role of vertical configural relations in face 'barcodes' for recognition. Journal of Vision 2014;14(10):574. doi: 10.1167/14.10.574.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Humans are experts at face recognition. The mechanisms underlying this complex capacity are not fully understood. Recently, it has been proposed that face recognition may be supported by a coarse-scale analysis of visual information contained in horizontal bands of contrast distributed along the vertical image axis – a biological facial 'barcode' (Dakin & Watt, 2009). A critical prediction of the face barcode hypothesis is that the distribution of image contrast along the vertical axis will be more important for face recognition than image distribution along the horizontal axis. A series of experiments are presented examining famous face recognition impairments from selectively disrupting image distributions along the vertical or horizontal image axis using a novel animation paradigm. Results showed that disrupting the image distribution along the vertical image axis was more disruptive for recognition than matched distortion along the horizontal axis. Consistent with the barcode hypothesis, these results suggest that human face recognition relies disproportionately on appropriately scaled distributions of image contrast along the vertical image axis. These findings contribute to an emerging understanding of low-level models of human face recognition.

Meeting abstract presented at VSS 2014

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