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

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