Emotion Recognition and Visual Matching Algorithms, Augmented Reality Makeup Try-on and 3D Body Scanning Technologies, and Artificial Neural Networkbased Neuromorphic Computing and In-Sensor Motion Perception Systems for Physical Appearance and Attra
Emotion Recognition and Visual Matching Algorithms, Augmented Reality Makeup Try-on and 3D Body Scanning Technologies, and Artificial Neural Networkbased Neuromorphic Computing and In-Sensor Motion Perception Systems for Physical Appearance and Attra

Summary/Abstract: The objective of this paper is to systematically review 3D body sensory and hyper-realistic augmented reality beauty technologies, deep learning based realistic-sounding synthetic voices, and digital clothing try-on apps. The findings and analyses highlight that augmented reality face filters further negative behaviors and sentiments, body shape and attractiveness, social appearance comparisons, and cognitive and affective engagement. Throughout June 2024, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “physical appearance and attractiveness” + “emotion recognition and visual matching algorithms,” “augmented reality makeup try-on and 3D body scanning technologies,” and “artificial neural network-based neuromorphic computing and in-sensor motion perception systems.” As research published between 2017 and 2024 was inspected, only 166 articles satisfied the eligibility criteria, and 29 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: Colandr, JBI SUMARI, PICO Portal, Rayyan, SRDR+, and SWIFTActive Screener.

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