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metadata language: English

Face Shape and Personality

Version
1
Resource Type
Dataset : experimental data, survey data
Creator
  • Kachur, Alexander (BestFtMe ltd)
  • Davydov, Denis (Modern University for the Humanities)
Publication Date
2019-01-02
Free Keywords
Human face; Big Five personality traits; Face and Behavior; Face; Machine Learning; Face and Personality
Description
  • Abstract

    Applying Machine Learning, we are looking for a number of elusive lincks between appearance and behavior. The dataset obtained after the moderation of images and validation of questionnaires. The test (holdout) dataset included self-reported and predicted from face shape by a ML algorithm the ​​Big Five personality traits for 505 males and 740 females.
  • Technical Information

    Presence of Common Scales: AgreeablenessConscientiousness
    Extraversion
    Neuroticism
    Openness

Temporal Coverage
  • 2016-01-01 / 2018-12-31
    Time Period: Fri Jan 01 00:00:00 EST 2016--Mon Dec 31 00:00:00 EST 2018
Geographic Coverage
  • Russian Federation
Sampled Universe
Anonymous participants  volunteers recruited through social network advertisements completed Big Five questionnaire and provided face photo.
Sampling
The initial sample included 25,202 participants who completed the questionnaire and uploaded a total of 77,346 photographs. The final dataset after the data screening procedures comprised 12,447 valid questionnaires and 31,367 associated photographs. 
This is a Validation dataset containtd randomly selected 10% (1290) of paticipant.
The participants ranged in age from 18 to 60 (59.4% females, M= 27.61, SD = 12.73 and 40.6% man M= 32.60, SD = 11.85).
Collection Mode
  • Validation dataset containing randomly selected 10% (1290) of paticipant.

Availability
Download

Update Metadata: 2019-03-27 | Issue Number: 1 | Registration Date: 2019-03-27

Kachur, Alexander; Davydov, Denis (2019): Face Shape and Personality. Version: 1. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E109082V1