Emotion detection is also a critical part of the development of human-computer interaction and mental illness treatment, where the ability to detect and respond to emotional states in real-time can have profound impacts on user experience, as well as emotional outcomes. The conventional emotion recognition systems generally cannot perform at high accuracy, particularly in real-time processing, due to challenges in extracting and fusing suitable features from facial expressions. This work introduces ResVGGNet, a novel hybrid Deep Learning (DL) model that can address these issues by integrating the advantages of VGG16 for effective feature extraction and ResNet for residual learning for deep learning, for better performance in real-time emotion recognition. With the accuracy of 98.60%, precision of 98.61%, and a low False Negative Rate of 0.23%, the ResVGGNet model offers high and stable performance with various emotional states. Experimental results verify the model's ability to simplify and thus fit perfectly in emotion-aware assistive technology. This article describes the capacity for improving users' well-being and affect regulation through real-time, dynamic feedback.
Sattar et al. (Wed,) studied this question.