Facial Recognition-Based Product Recommendation System Using Past Purchases

In an era marked by an explosion of digital data and the rapid advancement personalized product recommendations have emerged as a cornerstone of e-commerce. Leveraging cutting-edge technologies, this research presents an innovative approach to enhancing the shopping experience through a Facial Recognition-Based Product Recommendation System using a customer's past purchase history. This research delves into the development of a novel algorithm, K-Nearest Neighbors (KNN), to analyze and identify correlations between facial features and purchase patterns. By harnessing the power of computer vision and deep learning, the system extracts valuable insights from users' facial expressions, which are often indicative of their emotional responses during the purchase process. The system, through robust machine learning techniques, continuously adapts and refines its recommendations based on users' real-time facial expressions and historical purchase data, ensuring a dynamic and personalized shopping experience. Moreover, the utilization of facial recognition technology enhances user convenience by eliminating the need for tedious manual input, leading to a seamless and engaging shopping journey. This research represents a significant advancement in the realm of e-commerce, underscoring the potential for a highly personalized and emotionally attuned shopping experience. As we move towards a future where user-centric shopping becomes the norm, this Facial Recognition-Based Product Recommendation System stands at the forefront, offering a glimpse into the future of retail.

 Keywords: KNN, collaborative filtering. OpenCV.