A DEEP LEARNING FACIAL EXPRESSION RECOGNITION BASED SCORING SYSTEM FOR RESTAURANTS

Abstract

In this research, we provide a new method for gauging diners' happiness using a facial
expression detection system that relies on deep learning. Our technology uses cutting-
edge convolutional neural networks (CNNs) to assess user happiness in real-time based
on facial expressions recorded by in-house cameras. The method's stated goals include
improving operational efficiency, providing management with relevant insights into
client experiences, and raising the bar for service quality. In order to prove the system's
efficacy and possible uses, we go over its design, data gathering, execution, and
assessment outcomes. There has been a recent uptick in the popularity of fully
automated eateries. Since no one is there to ask guests about their experiences, it's
impossible to gauge how they feel about the restaurant's idea. To that end, this research
introduces a grading system that uses pre-trained convolutional neural network (CNN)
models to identify facial expressions. A web server, a pre-trained AI server, and an
Android mobile app make it up. It is appropriate to assess both the cuisine and the
atmosphere. There are now three possible outcomes supplied by the rating system:
pleased, neutral, and dissatisfied.