University of Wollongong
Browse

Likeness, Familiarity, and the Ambient Portrait Average

Download (2.72 MB)
journal contribution
posted on 2024-11-15, 01:18 authored by Susan Hayes, Peter CaputiPeter Caputi, T Zaracostas, Maggie Henderson, Julie Telenta, Elspeth McCombe, Kim Christopher, Emma Calvert, Donna Abbati, Odette Smith, Emma Medwell, Joyce Wilcock
© The Author(s) 2020. This artist-led research project involved 10 visual artists producing 10 ambient portraits and a portrait average of a locally familiar Sitter, and 10 ambient portraits and a portrait average of a less locally familiar Sitter. All were then assessed for likeness by more than 150 members of the general public attending an exhibition during Australia’s 2018 National Science Week. The results of this study are that portrait averages can be highly shape accurate and tend to be seen as a good likeness by all viewers. However, the portrait average is not necessarily the best likeness. Extending and validating our previous findings regarding the relationship of likeness, familiarity, and shape accuracy (as measured using geometric morphometrics) in portraiture, unfamiliar viewers favouring shape accurate depictions of a Sitter attained statistical significance. Familiar viewers, however, although also tending to view shape accurate depictions a good to very good likeness, were shown to have a stronger preference for portraits that exaggerate a Sitter’s facial distinctiveness, including an exaggeration of their head pose, providing such exaggerations are in approximate proportional agreement.

History

Citation

Hayes, S., Caputi, P., Zaracostas, T., Henderson, M., Telenta, J., McCombe, E., Christopher, K., Calvert, E., Abbati, D., Smith, O., Medwell, E. & Wilcock, J. (2020). Likeness, Familiarity, and the Ambient Portrait Average. Perception,

Journal title

Perception

Volume

49

Issue

5

Pagination

567-587

Language

English

RIS ID

142651

Usage metrics

    Categories

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC