Fashion Recommendation System and Virtual Trial Room

Abstract

The fashion industry has recently witnessed massive growth in the clothing and textile industries. In e-commerce marketplaces, where there are many options accessible, relevant product material or information can be filtered, arranged, and efficiently communicated to users. As the standard of living increased, people's attention began to shift towards fashion, which is considered to be a common form of aesthetic expression. The development of the fashion business over time has been influenced by this human tendency. The abundance of clothing alternatives on e-commerce sites, however, has made it more difficult for customers to select the ideal outfit. In this article, we describe a personalised fashion recommender system that generates suggestions for the user based on input. In order to enhance the consumer shopping experience, we developed a software application that combines a recommender system with the OpenCV and TensorFlow technologies. This will enable customers to virtually try on clothing without having to wait in a large queue. The application uses OpenCV to map the clothes onto the customer’s body, the customer can then have a look at it and decide whether to buy the clothes or not. According to the results, there is no need to visit trial rooms because the mapping is done precisely onto the user's body. Thus, our app offers a quick, simple, and accurate method of trying on clothing, and we think that will help shape the retail sector.