ABSTRACT. This paper provides a systematic literature review of studies investigating augmented virtuality technologies, deep learning-based facial retouching, and body beautification tools. The analysis highlights that deep learning algorithms and visual data enable attractive physical features. Throughout April 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “augmented reality beauty apps for idealized facial and bodily appearances” + “low self-esteem and self-image,” “cognitive and affective engagement,” and “negative emotional states.” As we inspected research published between 2017 and 2022, only 163 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 25, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, MMAT, and ROBIS.

Keywords: augmented reality beauty app; idealized facial and bodily appearance; low self-esteem and self-image; cognitive and affective engagement; negative emotional state

How to cite: Corpodean, H., Tudosă, P., Popescu, K. C., and Petreanu, A.-M.-S. (2022). “Augmented Reality Beauty Apps for Idealized Facial and Bodily Appearances: Low Self-Esteem and Self-Image, Cognitive and Affective Engagement, and Negative Emotional States,” Journal of Research in Gender Studies 12(2): 79–94. doi: 10.22381/JRGS12220225.

Received 20 June 2022 • Received in revised form 23 December 2022
Accepted 27 December 2022 • Available online 30 December 2022