Abstract: The "Human Emotion Detection for Hotel Feedback System" project is designed to revolutionize the way customer feedback is collected and analyzed in the hospitality industry. This system integrates advanced technologies to provide a seamless and efficient feedback collection process. The core components of the system include a web-based interface, image processing, and machine learning algorithms to detect and analyze customer emotions. The system comprises three main user roles: Admin, Staff, and Customer. Admins can log in through a frontend developed using AngularJS and C#, allowing them to add feedback questions categorized by type and view the collected feedback. Staff members also use the same frontend technologies to log in and review customer feedback. Customers provide feedback through a camera interface that captures their image, and the system processes these images to detect emotional responses. The backend system is built using Python, leveraging machine learning technologies. Specifically, a Convolutional Neural Network (CNN) algorithm is employed for image detection and emotion recognition. The captured images are analyzed to determine the customer's emotional state, and this information, along with the feedback, is stored in a database.

Keywords: Image Processing, Machine Learning, Convolutional Neural Network (CNN), Customer Feedback.

| DOI: 10.17148/IARJSET.2024.11812