Mirror for Facial Skin Disease Diagnosis: Revolutionizing Skin Health

Abstract

This paper presents an innovative smart mirror system designed to assist in the preliminary diagnosis of facial skin diseases using artificial intelligence and computer vision techniques. The proposed system integrates a two-way mirror with an embedded camera, Raspberry Pi processing unit, and a deep learning model trained on a diverse dataset of dermatological conditions including acne, eczema, psoriasis, rosacea, and melanoma. The mirror captures real-time facial images, processes them through a Convolutional Neural Network (CNN) based classifier, and displays diagnostic results along with recommended actions directly on the mirror's display surface.

Our system achieves an overall classification accuracy of 93.7% across eight major skin disease categories, with a detection latency of under 2 seconds. The smart mirror eliminates the barrier of specialized equipment and provides accessible, non-invasive, and real-time skin health monitoring in everyday environments such as bathrooms and clinics. This paper describes the system architecture, deep learning methodology, implementation details, experimental results, and discusses the significant potential of AI-integrated smart mirrors in revolutionizing preventive dermatological care.