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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM48565

Beauty Buzz: Advanced Real-Time Virtual Makeup and Personalized Recommendations

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM48565

Volume:

9 |

5 |

May 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM48565

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

DRK

Dr. Rupinder Kaur

TB

Tanisha Bansal

SKG

Shashank Kumar Gupta

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Abstract

Virtual makeup tools have become a significant innovation in the beauty sector, allowing individuals to explore various cosmetic looks in a personalized and interactive way. Beauty Buzz represents a cutting-edge virtual makeup solution that merges deep learning techniques with computer vision to create a smooth, real-time user interface. The platform leverages Convolutional Neural Networks (CNNs) alongside OpenCV for accurate facial landmark detection and real-time cosmetic application. Additionally, Beauty Buzz provides personalized makeup suggestions based on the user’s intended occasion and budget, improving both usability and user engagement. This paper offers an in-depth overview of the system’s architecture, facial mapping techniques, user behavior analysis, and findings from survey-based research. It also examines the relationship between self-esteem, makeup usage, and user satisfaction, shedding light on the psychological and emotional effects of virtual makeup technologies.

KEYWORDS

Virtual Makeup Deep Learning Self-Esteem Facial Feature Detection Augmented Reality User Satisfaction

Cite This Article

Dr. Rupinder Kaur, Tanisha Bansal, Shashank Kumar Gupta, "Beauty Buzz: Advanced Real-Time Virtual Makeup and Personalized Recommendations," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 5, May 2025. https://doi.org/10.55041/IJSREM48565

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Publication Date

5/23/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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0

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3

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5

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The authors declare no conflict of interest related to this research work.

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