Introduction to Emotion Detection and Predictive Psychology in the Age of Technology

Abstract

A performance study of a deep unified model for a facial expression system using hybrid models like ResNet and DenseNet in a CNN-based image classification framework is crucial. Assessing the effectiveness of this complex system that interprets facial expressions to identify emotions is crucial. Data is carefully prepared and separated to produce a balanced and representative dataset. The hybrid ResNet-DenseNet model architecture is crucial. It must be customised for face expression recognition. Data augmentation, hyper parameter adjustment, and regularisation are used to optimise model performance during training. Accuracy, precision, recall, F1-score, and confusion matrix are used to evaluate the model's performance. A different dataset may be used to evaluate its generalisation skills. Comparing the model to others might reveal its performance.