Implementasi Machine Learning untuk Pengenalan Penggunaan Makeup - Wulandari, Fetricia; Ferdian, Rian (Universitas Andalas, 2025)
Learning makeup can be challenging, especially for beginners who need to understand face shapes, color gradients, and composition. This study presents a machine learning-based system designed to recognize face shapes and recommend makeup styles. Key features include video tutorials tailored to the user’s face shape and LED indicators that show the products to use, allowing users to practice accurate makeup techniques. The system is powered by a Raspberry Pi 4 Model B, with a webcam for capturing facial images. It uses the Haar Cascade algorithm for face detection and a Support Vector Machine (SVM) classifier to categorize faces into three main shapes: round, oval, and square. Additionally, the system offers two makeup styles: flawless and bold, making it accessible and versatile. Testing results show that the system accurately detects and classifies face shapes, providing recommendations that simplify makeup learning. Beginners can follow step-by-step tutorials and use the system’s interactive features to learn essential techniques, reducing the risk of mistakes. Overall, this study offers an innovative solution for those learning makeup, with potential for further development to support additional face shapes and makeup styles. The use of machine learning enhances the user experience, providing guidance that combines ease of learning with professional-level results.