Facial recognition and classification for customer information systems: a feature fusion deep learning approach with FFDMLC algorithm

A customer information system (CIS) is a crucial component of a customer relationship management (CRM) system. The CIS collects, stores, and manages customer data to enhance the customer experience and sales. Deep learning technology has been used to develop facial expression recognition systems that can accurately identify and analyze facial expressions. By integrating facial expression recognition deep learning into a CIS for CRM, businesses can gain a deeper understanding of the emotions and behavior of the customers, allowing them to provide more personalized experiences. The data collected by the CIS can then be used to personalize marketing campaigns, tailor product recommendations, and improve customer service interactions. The integration of facial expression recognition deep learning into a CIS for CRM has the potential to revolutionize the way businesses interact with their customers. This paper constructed a Feature Fusion Deep Multi-Layer Classification (FFDMLC) model for facial expression recognition for the CIS system. With FFDMLC model comprises the feature-fusion model in face recognition. The propsoed FFDMLC approach fuses various features and estimates the feature fusion model. The FFDMLC model employs deep learning for feature computation and expression classification in individuals. The hyperparameters of this proposed method are optimized using the COOT optimization method. This process optimizes reaction conditions, leading to increased yields, minimized by-products, and improved overall efficiency. The CIS model uses the facial expression recognition system for the computation of facial features in the video sequences. The proposed FFDMLC model is evaluated based on the consideration of two datasets such asCK + and the FER2013 dataset, which also exhibits a higher recognition rate of 99.93% for the understanding opinion of the customers.