ABSTRACT. This paper draws on a substantial body of theoretical and empirical research on beauty filter-based appealing self-presentation and 3D facial avatar makeup simulation tools, emotion recognition and visual sentiment algorithms, and 3D human body and image appearance modeling for augmented reality-based virtual try-on and hyper-personalized beauty experiences. In this research, prior findings were cumulated indicating that 3D augmented reality try-on and biometric emotional artificial intelligence technologies enable physical condition detection and monitoring, beauty filter-based appealing self-presentation, idealized beauty representations and images, perceived facial attractiveness, and social media-related body dissatisfaction. Study screening tools, reference management software, and machine learning classifiers leveraged include CADIMA, JBI SUMARI, Litstream, METAGEAR package for R, Nested Knowledge, and SWIFT-Active Screener. The case studies cover Everlook, Wondershare Filmora, AlterCam, Sandpiper Studio Beauty & Filter Camera, PhotoDirector artificial intelligence beauty and face filter app, FaceApp, FixThePhoto, YouCam Perfect face editing tools, BeautyPlus, and Facetune.

Keywords: beauty filter-based appealing self-presentation; 3D facial avatar makeup; 3D human body; image appearance; augmented reality-based virtual try-on; hyper-personalized beauty experience

How to cite: Pârvu, I. A., Privitera, D., Bîgu, A.-M., Pîrcălăbescu, R. M., and Pațurcă, R. N. (2025). “Beauty Filter-based Appealing Self-Presentation and 3D Facial Avatar Makeup Simulation Tools, Emotion Recognition and Visual Sentiment Algorithms, and 3D Human Body and Image Appearance Modeling for Augmented Reality-based Virtual Try-on and Hyper-Personalized Beauty Experiences,” Journal of Research in Gender Studies 15(1): 49–56. doi: 10.22381/JRGS15120255.

Received 12 February 2025 • Received in revised form 14 July 2025
Accepted 17 July 2025 • Available online 24 July 2025