Recommendation system based on facial expression

Abstract

Understanding human emotions relies heavily on interpreting facial expressions, which are a crucial aspect of non-verbal communication. Yet, the intricacies of facial muscle movements make it a formidable task to accurately model these expressions when working with static images, especially when considering different facial poses. To tackle this challenge, we introduce an innovative method that harnesses 3D technology to capture and decode facial expressions more effectively. Moreover, we propose a contour alignment technique designed to enhance modeling precision by autonomously repositioning 3D contour landmarks to align with static images. The acquired input representing facial expressions can be parametrically adjusted, enabling the creation of a diverse range of facial expressions through an expression transfer approach grounded in the Facial Action Coding System (FACS).In an era marked by the explosive growth of the digital landscape and an overwhelming deluge of information, the quest for a reliable method to offer tailored recommendations from this vast sea of data has become an imperative. In response to this challenge, recommendation systems have been engineered. One intriguing avenue involves the utilization of pretrained models such as VGG16, harmoniously amalgamated to forge a tool that could prove immensely advantageous for humanity.We envision that our system will extend its benefits to diverse sectors, encompassing the prediction of films, music selections, and various commodities, among others. The adaptability of this system holds the potential to elevate decision-making processes and furnish invaluable guidance across domains.