A ROBUST CONVOLUTIONAL NEURAL NETWORK-BASED APPROACH FOR HUMAN EMOTION CLASSIFICATION: CROSS-DATASET VALIDATION AND GENERALIZATION

Abstract

This paper presents advancements in the field of facial expression recognition, emphasizing the development and application of machine learning algorithms to enhance the automated understanding of human emotions. We detail our methodological approach, which integrates deep learning techniques with real time image processing to identify and classify a wide range of facial expressions across diverse human populations. The core of our research lies in the creation and validation of a robust model trained on an extensive dataset that includes varied emotional states captured under different environmental conditions. Our findings demonstrate significant improvements in accuracy and speed over existing systems, highlighting the model's adaptability to both static images and dynamic video streams. We also address the challenges related to the interpretability of machine learning models in this context and propose solutions to increase  transparency and reliability. The implications of this technology for applications in sectors such as security, healthcare, and customer service are discussed, along with ethical considerations concerning privacy and the potential for bias. The paper concludes with suggestions for further research to refine these technologies and expand their practical applications. The proposed model of facial expression recognition can deal with subjects of any ethnicity.