A Hybrid HSV and YCrCb OpenCV-based Skin Tone Recognition Mechanism for Makeup Recommender Systems

Skin detection technology serves multiple purposes across various sectors, including surveillance, criminal justice, and healthcare. This study focuses on skin detection by extracting RGB values of skin tones from facial images of diverse ethnic backgrounds. Utilizing a three-tier OpenCV-based architecture, the approach encompasses skin detection, skin tone identification, and is tested in an application for recommendation of the most fitting makeup foundation shade, brand, and product. The research evaluates three color-space models for their effectiveness in skin and skin tone detection: HSV (Hue, Saturation, Value) with Gaussian blur, HSV alone, and a combination of HSV and YCrCb enhanced with gamma correction and image segmentation. The precision of each color space method was evaluated by measuring the difference between the predicted RGB skin tone values and the actual RGB skin tone values, utilizing the Delta-E metric for comparison. The hybrid model combining HSV and YCrCb color spaces emerged as the most accurate, achieving the lowest Delta-E average value of 16.68, thereby surpassing the other methodologies.