Augmented Reality Beauty Apps for Idealized Facial and Bodily Appearances: Low Self-Esteem and Self-Image, Cognitive and Affective Engagement, and Negative Emotional States
Augmented Reality Beauty Apps for Idealized Facial and Bodily Appearances: Low Self-Esteem and Self-Image, Cognitive and Affective Engagement, and Negative Emotional States

Summary/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.

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