Virtual Try On Hairstyles Using Augmented Reality

Abstract-The advancement of augmented reality (AR) has run through most sectors, including fashion and beauty. It is difficult to select hairstyles for individuals since there is no way of really knowing how the chosen style will appear on the person. This paper introduces a Virtual Try-On Hairstyle solution using Augmented Reality (AR) technology to allow users to experiment with various hairstyles in real-time prior to making hairstyle decisions.This solution optimally utilizes computer vision, machine learning, and AR platforms to superimpose hairstyles onto a user's image or live video smartly and dynamically. By leveraging deep learning-based facial recognition and real-time hair tracking, the system ensures accurate and seamless overlay of hairstyles. Moreover, users can interact with and modify hairstyle options, further empowering them to make improved styling choices.The solution addresses the problem by improving user experience through an interactive and personalized interface minimizing frustration and maximizing decision-making at hair salons. The system also provides salon professionals with a tool to suggest styles based on individual facial features and hair types. Performance assessment shows that the system is extremely accurate at real-time hair tracking, maintaining smooth rendering at 30 FPS, and provides users with an extremely immersive and engaging interactive experience.In general, this project offers a business and artistic solution to the beauty and hair market, bridging technology with style visualization on an individual basis to offer users confidence and satisfaction regarding their hairstyle choices.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-21
DOI
https://doi.org/10.5281/zenodo.15479757
Primary Topic
Statistical Methods in Epidemiology
Type
article
Field-Weighted Citation Impact
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article

Virtual Try On Hairstyles Using Augmented Reality

Zenodo (CERN European Organization for Nuclear Research)
Statistical Methods in Epidemiology
article

Virtual Try On Hairstyles Using Augmented Reality

article en

Abstract

Abstract-The advancement of augmented reality (AR) has run through most sectors, including fashion and beauty. It is difficult to select hairstyles for individuals since there is no way of really knowing how the chosen style will appear on the person. This paper introduces a Virtual Try-On Hairstyle solution using Augmented Reality (AR) technology to allow users to experiment with various hairstyles in real-time prior to making hairstyle decisions.This solution optimally utilizes computer vision, machine learning, and AR platforms to superimpose hairstyles onto a user's image or live video smartly and dynamically. By leveraging deep learning-based facial recognition and real-time hair tracking, the system ensures accurate and seamless overlay of hairstyles. Moreover, users can interact with and modify hairstyle options, further empowering them to make improved styling choices.The solution addresses the problem by improving user experience through an interactive and personalized interface minimizing frustration and maximizing decision-making at hair salons. The system also provides salon professionals with a tool to suggest styles based on individual facial features and hair types. Performance assessment shows that the system is extremely accurate at real-time hair tracking, maintaining smooth rendering at 30 FPS, and provides users with an extremely immersive and engaging interactive experience.In general, this project offers a business and artistic solution to the beauty and hair market, bridging technology with style visualization on an individual basis to offer users confidence and satisfaction regarding their hairstyle choices.

Zenodo (CERN European Organization for Nuclear Research)
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Openalex Percentile: Top 54%
Statistical Methods in Epidemiology
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Virtual Try On Hairstyles Using Augmented Reality · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS