Facial Emotion Recognition for Virtual Customer Service Agents

Due to the rapidly changing customer support environment, firms are using virtual agents to improve customer interactions, reduce response times, and deliver better solutions to customer questions, concerns, and issues. Virtual agents must understand and respond to user emotions to enable more human-like interactions. Face recognition, or Facial Expression Recognition (FER) holds significant importance in the field of computer vision and artificial intelligence to understand the emotions of the eyes. Deep learning is used to classify face expressions into emotion categories in this study. We train and test our models on the CK+ dataset for high accuracy. We also offer an interactive online interface for real-time emotion recognition using the Streamlet library. FER improves virtual customer care agents' emotional intelligence and user experience, as shown by our studies.