Personalized Skin Type Classification and Recommendations

This paper presents a novel AI-driven web-based system for personalized skincare solutions through automated skin type classification and tailored product recommendations. Leveraging deep learning techniques, specifically a pre-trained MobileNetV2 model, the system analyzes user-uploaded facial images to accurately classify skin types such as oily, dry, combination, and acne-prone. This classification enables the generation of customized skincare product suggestions from a curated database, addressing individual skin conditions with precision. The platform integrates user management features allowing both administrators and users to interact seamlessly, enhancing usability and content relevance through dynamic FAQ management. By combining computer vision with machine learning, the proposed approach overcomes traditional challenges in subjective skin assessment and inefficient product selection, providing an accessible, data-driven tool that empowers users to optimize their skincare routines. Experimental results demonstrate high classification accuracy and improved user satisfaction, highlighting the potential of AI to transform personal care through personalized recommendations. This work contributes to the intersection of healthcare and artificial intelligence by offering an efficient, scalable solution for individualized skin health management.