Facial emotion-based movie recommendation system using optimized compound scaling neural network with polynomial and RBF kernels.

Facial Emotion Recognition (FER) has emerged as an important research topic in human-computer interaction (HCI) with advances in machine learning and deep learning. FER is used across various fields, including healthcare, marketing, gaming, education, security, and real-time human-robot interaction. One real-time application of FER is a movie recommendation system that suggests movies based on users' emotions. In this paper, we have used an optimised compound-scaling neural architecture with polynomial and radial basis function (RBF) kernels for facial emotion recognition. To implement this research, we have used four diversified facial emotion datasets. All four datasets are subjected to pre-processing, feature extraction, and classification. All four datasets utilise different SVM algorithms for emotion recognition, and each achieves a different level of accuracy. Various visualization techniques, such as t-SNE and Grad-CAM, are used to analyze feature space separability and improve model interpretability. The maximum accuracy of the proposed model is 89.68%. Using our customised CNN model, we can predict seven facial emotions: happy, sad, angry, fearful, surprised, neutral, and disgusted. The proposed model can be used in various settings where real-time facial emotion recognition plays a vital role.