Arani Aravinthan and Madushi Pathmaperuma, United Kingdom

Beauty is a power which allows people to express themselves, gain self-confidence and open to others. Usage of beauty products can help this by creating a new look to uplift the character. Choosing the right makeup product is not an easy task in this diverse range of products these days. Intelligent systems for beauty and makeup selection have gained significant research interest in recent years. Most existing models focus on detecting prominent facial features such as skin tone, lip colour, and overall facial structure. However, minor yet impactful areas, such as the delicate regions around the eyes, are often overlooked. These areas play a crucial role in defining facial aesthetics, influencing expressions, and enhancing overall appearance. To address this gap, this system is designed to provide targeted recommendations for eye-focused beauty enhancements, ensuring a more comprehensive and personalized approach to makeup selection. The proposed system will recommend makeup products considering personal traits of the user such as the length and volume of the eyelashes. A new approach has been devised in calculating the length of the eyelashes aiding the use of advanced computer vision techniques like edge detection, and a regression-based Convolutional Neural Network (CNN) model is trained for prediction. A Support Vector Machine (SVM) is used for the classification task in recommending products for eyelash care.

Edge Detection, Contour Detection, Support Vector Machine