A study on AI-powered facial analysis for types of skin acne detection and oily-ness assessment and personalized product recommendations

Acne vulgaris is a widespread dermatological condition that can lead to scarring and psychological distress, necessitating accurate and timely diagnosis. Traditional clinical assessments are often subjective and inaccessible, whereas AI-powered solutions leverage deep learning architectures such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and YOLO-based object detection models to automate acne identification, lesion segmentation, and severity classification with high precision. Generative Adversarial Networks (GANs) and Self-Supervised Learning (SSL) further enhance model performance by improving dataset diversity and reducing annotation dependency. Beyond detection, AI-driven personalized skincare recommendations use machine learning techniques like Collaborative Filtering, Content-Based Filtering, and Reinforcement Learning to analyze skin type, acne progression, environmental factors, and treatment history for optimized product suggestions. Transformer-based Natural Language Processing (NLP) models refine recommendations by processing dermatological research, clinical guidelines, and user reviews, while federated learning ensures data privacy.