Modelling Subjective Attractiveness

Konrad Lewszyk and Piotr Wójcik
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Konrad Lewszyk: University of Warsaw, Faculty of Economic Sciences and Data Science Lab WNE UW

No 2023-06, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: Attractive people obtain greater economic and reproductive success. This article attempts to grasp individual preferences of facial attractiveness and create reliable models that will accurately predict a beauty score on a binary and quintary scale. Based on extensive research conducted on factors of attractiveness, we derive the most important facial features that have the highest impact in beauty perception. Based on a sample of 681 images of faces using facial a landmark detector. We derive various numerical features represented by face characteristics and. The application of various machine learning algorithms shows that attractiveness can be predicted accurately based on facial characteristics. In addition, we show that indeed the attractiveness is subjective as the same features have different importance for different subjects.

Keywords: Attractiveness; beauty-premium; image processing; machine learning; predictive models (search for similar items in EconPapers)
JEL-codes: C40 C53 J71 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2023
New Economics Papers: this item is included in nep-big and nep-cmp
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https://www.wne.uw.edu.pl/download_file/2564/0 First version, 2023 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2023-06

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