Real Time Customer Satisfaction Analysis using Facial Expressions and Headpose Estimation

Abstract

One of the most exciting, innovative, and promising topics in marketing research is the quantification of customer interest. This work focuses on interest detection and provides a deep learning-based system that monitors client behaviour. By assessing head position, the recommended method assesses customer attentiveness. Customers whose heads are directed toward the promotion or the item of curiosity are identified by the system, which analyses facial expressions and records client interest. An exclusive method is recommended to recognize frontal face postures first, then splits facial components that are critical for detecting facial expressions into iconized face pictures. Mainly consumer interest monitoring will be executed. Finally, the raw facial images are combined with the iconized face image's confidence ratings to estimate facial emotions. This technique combines local part-based characteristics through holistic face data for precise facial emotion identification. The new method provides the dimension of required marketing and product findings indicate that the suggested architecture has the potential to be implemented because it is efficient and operates in real time.