AI Powered Skin Care Assistant for Personalised and Ethical Product Selection

Consumers often find it challenging to choose the right skincare products. This is due to unclear ingredient lists, misjudging their skin type, and misleading marketing. Suncreens, in particular, have a wide range of formulations, potential irritants, and may not suit specific skin conditions like acne or dryness. This work suggests an AI-powered system that uses computer vision, CNN-based classification, YOLO-based acne detection, and OCR for ingredient analysis to deliver personalized, clear, and ethically responsible sunscreen recommendations. The system identifies skin type from facial images using an EfficientNetV2 classifier and detects acne presence with YOLOv8. It gathers product information through OCR and connects ingredients to a curated cosmetic safety database to determine vegan status, comedogenicity, and allergenicity. A rule-based scoring model combines skin analysis and ingredient evaluations to provide tailored sunscreen suggestions. Testing in different lighting conditions and on various skin tones shows strong region detection, reliable OCR extraction, and useful recommendations that follow dermatology standards. The prototype illustrates the potential of multimodal AI in skincare decision support and lays the groundwork for future developments in dermatological applications.