Abstract:
The fashion and apparel industry faces complex challenges in delivering personalized styling service that account for the relationship among physical attribute, circumstances, and individual inclinations. The whole process of dressing up has numerous interdependent variables that affect what the individual wears. These are the corroboration of skin tone with weather, the fittingness of events, and the need of culture and the self-expression of aesthetics. The traditional form of shopping and the existing fashion technologies fail to provide comprehensive guidelines on these dimensions. They in particular do not focus on such important aspects as skin tone, undertone, face shape, and body proportions. The absence of this gap results in bad choices, ineffective wardrobes, and insecurity, as well as repeated fashion mistakes. Users do not know what colors, outfits and trends to use without the help of real time experts.
Modern strategies such as using the Flutter framework for cross-development, ensuring compatibility across both android and iOS devices, marked the development process. Forthcoming features include and a modern Material 3 design theme to enhance user engagement and functionality.
The thesis develops an intelligent AI-powered fashion recommendation system for Pakistani market. Mobile application uses OpenCV for facial features analysis including skin tone, undertone, eye color, hair color and face shape detection. It provides scientifically-backed seasonal color palettes and personalized styling with digital wardrobe system. Application scrapes fashion data from Pakistani brands Khadi, Sapphire, Gul Ahmed, Nishat Linen and Akaram. Flutter-Dart app with Firebase. Intelligent Dialog flow chatbot in English-Urdu. Fit Me analyzes body type and scores of the user pic.
The report outlines the technical strategies adopted to develop these features, the methods used in addressing them and the results of the built application. It also discusses recommendations made regarding future updates and improvements, which focus on the improvement of accessibility, contents, and the overall user experience.
