Artificial Intelligence-Based Facial Expression Recognition for Identifying Customer satisfaction on Products

Facial Expression Recognition (FER) for Identifying Customer Satisfaction on Products is one of the most powerful and challenging research tasks in social communication. Artificial intelligence (AI)-based emotion recognition harnesses the collective strength of machine learning, deep learning, and computer vision to decipher the subtleties of human emotions. By intricately analyzing facial expression, including the nuanced movements of the mouth, eyes, and eyebrows. Recent innovations have driven notable progress in face detection and recognition that enhance performance and reliability. This study focuses on leveraging AI-based facial expression recognition to identify customer satisfaction with products. The objective of this research is to develop a robust and accurate facial expression recognition system capable of analyzing customer emotions and determining their satisfaction levels based on their facial expressions. The proposed study used a hybrid convolutional neural network (CNN) and deep neural networks (DNN) model to extract meaningful features from facial images and classify them into different emotional states. The trained model is to be evaluated using a separate test dataset to measure its performance in accurately recognizing customer emotions and assessing satisfaction levels. The evaluation metrics include accuracy, precision, recall, and F1-score. The proposed experiment achieved excellent result with a real-time image-based dataset.