• Shubhangi Chavan
• Aditya Kotkar
• Sahil Panchal
• Pratik Gupta
• Rupali Rana

This project aims to develop a Personalized Fashion Recommender System using advanced computer vision and artificial intelligence technologies. The system analyzes facial features such as skin tone, height, body size, and hair color to suggest outfits tailored to each individual. It employs color-matching algorithms to recommend clothing colors that complement the user's complexion. Image processing techniques estimate height and analyze body silhouette to suggest outfits that enhance proportions. Machine learning models predict clothing sizes and styles that align with the user's physique. The system provides a user-friendly interface for seamless image uploading and clear instructions for accurate feature extraction. Privacy and security measures are prioritized. The system generates fashion recommendations based on a comprehensive analysis of the user's unique features and fashion trends. It continuously refines recommendations through user feedback and adapts to evolving preferences. The system analyzes facial features, including skin tone, height, body size, and hair color, using local binary pattern algorithms for face recognition and OpenCV for feature extraction. To detect the color tone of a person in an image, we will use color space transformations and clustering algorithms such as K-Means Clustering. Machine learning models, such as SVM and neural networks, predict clothing sizes and styles aligned with the user's physique. Hybrid approaches combine aspects of both methods for more accurate recommendations, leveraging the strengths of collaborative and content-based filtering.

"Stylesync: Outfit Recommender", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.a130-a135, November-2024, Available :http://www.jetir.org/papers/JETIR2411013.pdf

"Stylesync: Outfit Recommender", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppa130-a135, November-2024, Available at : http://www.jetir.org/papers/JETIR2411013.pdf