Browsing by Author "Muhammad Faisal , Attique Hassan , Ayesha , Anoushe Adnan and Zainab Rizwan"

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    AI Based Fashion Advisor App (OUTFYT)
    (UMT, Lahore, 2025) Muhammad Faisal , Attique Hassan , Ayesha , Anoushe Adnan and Zainab Rizwan
    Fashion is a fundamental form of self-expression, yet many individuals struggle with selecting appropriate outfits due to overwhelming choices, lack of styling knowledge, and limited access to personalized recommendations. The AI-Based Fashion Advisor App, OUTFYT, addresses these challenges by integrating Artificial Intelligence (AI), Machine Learning (ML), and Facial Recognition to provide personalized outfit and grooming recommendations. The system utilizes deep learning models such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and AI-driven recommendation algorithms to analyze users' wardrobe collections, facial features, and contextual factors like weather and occasions. The app enhances styling decisions by offering tailored outfit suggestions, hairstyle and beard recommendations, and real-time fashion insights. The mobile application is developed using Flutter for cross-platform compatibility and connects to a Django-based backend that serves AI-generated recommendations via REST APIs. Firebase and PostgreSQL are used for secure data storage, while cloud services such as AWS EC2 and Google Cloud host the AI models to ensure scalability and real-time processing