Study and Analysis of Human Emotions Based on Consuming Food Using CNN Approach

In this project, we aim to use OpenCV, Python, HTML, and Flask to identify the facial expressions of customers while they are eating in restaurants. As food is a fundamental source of sustenance, it is essential to ensure customer satisfaction. With the help of this technology, machines can now understand the expressions and feelings of human beings. To tackle the issue of uncertainty in customer satisfaction, our project involves live prompting and a video uploading section, where the system verifies the expressions of customers while eating and predicts the probability of their satisfaction. To predict this, we used the CNN algorithm. This rating system provides valuable insights for restaurant owners to improve their offerings on a daily basis, which can lead to increased customer satisfaction and business growth. The system is designed to be non-intrusive and can function seamlessly in the restaurant environment without disturbing customers' dining experience. It leverages real-time video analysis and facial recognition techniques to continuously monitor emotional changes throughout the meal. Expressions such as happiness, disgust, surprise, and neutrality are mapped to a satisfaction index, which helps quantify the emotional response of each customer. INDEX TERMS: Open CV, Python, Jupiter Notebook, Anaconda Navigator, Feedback System, Face Recognition, Emotion detection, Web Development, Database