STYLOSCAPE: An AI-Powered Undertone and Color Palette Analysis System for Personalized Fashion Recommendations

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STYLOSCAPE: An AI-Powered Undertone and Color Palette Analysis System for Personalized Fashion Recommendations.

Anushka Ukil1, Debopriya Adhikary2, Avay Kumar Shaw3 , Sanjukta Chatterjee4

1Computer Science and Engineering & JIS College of Engineering

2Computer Science and Engineering & JIS College of Engineering

3Computer Science and Engineering & JIS College of Engineering

4Computer Science and Engineering & JIS College of Engineering

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Abstract - Fashion advice is usually based on broad trends and not individual color analysis, resulting in style choices that do not fully cater to the individual. Styloscape is an AI-powered system that measures an individual's undertone and provides customized color palette suggestions for fashion. The system retrieves an image through computer vision principles, analyzes the skin undertone (cool, warm, or neutral), and recommends an optimal color palette. Also, the system uses Selenium-based web scraping to fetch real-time dress suggestions from Myntra, so users can directly explore and purchase outfits within their suggested color palette. Streamlit and Python are used to develop the system, which enables a seamless and dynamic user experience. This study explores the efficacy of AI-powered undertone detection in fashion technology, where it can play a pivotal role in enhancing more tailored shopping experiences. Challenges like lighting conditions, dataset diversity, and web scraping limits are also suggested, as well as viable improvements for real-time processing and more extensive e-commerce integrations.

Key Words: Gemini, Computer Vision, Machine Learning, Undertone, Color Palette, Python

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