Facial Skincare Product Recommendation Using Deep Learning Techniques

Abstract

Skincare products are essential cosmetics, particularly in this day and age. Numerous online retailers offer a wide range of skincare products in their inventory. One issue with online skincare product purchases is that consumers are unable to test the product and must rely on reviews and ratings from other customers. To make this process easier and more effective, an innovative skincare product recommendation system has been developed. The system revolutionizes personalized skincare solutions by seamlessly integrating image processing and advanced deep learning techniques like Efficient Net B0. The system goes beyond traditional classifications, accurately identifying diverse skin types, including normal, oily, dry, sensitive or combination. It also takes a meticulous approach to assess skin tones. Users can effortlessly engage with this system through a user- friendly web interface, where they upload facial images. In return, they receive intricate recommendations tailored not only to their specific skin types but also to address individual concerns such as acne, pigmentation and dark circles. The suggestions provided are comprehensive, spanning a range of products including cleansers, moisturizers, serums and more. The system says the routine to the user. By offering personalized recommendations and valuable skincare routine with an overall accuracy of 92.34%.