Real-time emotion detection from integrating electroencephalography, facial expressions and speech: review

Emotion recognition systems have gained substantial focus due to their pivotal role in man-machine interaction and affective computing applications. This comprehensive literature survey explores the latest advancements in the field, spanning a different variety of strategies and datasets. Survey delves into realm of cross-corpus speech emotion identification, discussing innovative approaches including deep local domain adaptation and multimodal systems like RobinNet. Furthermore, it investigates electroencephalography-based emotion recognition techniques, highlighting hierarchical self-attention networks, deep forest models, and spatio-temporal convolution attention neural networks. The paper also presents collaborative frameworks for the diagnosis of sadness that makes use of cross-scale facial feature analysis and negative emotion detection. In realm of machine learning, ensemble approaches for affective computing and the efficacy of prompt consistency in multi-label textual emotion detection are examined. Through this survey, emerging trends, comparative studies, and validation frameworks in emotion recognition systems research are synthesised. The findings underscore the significance of these systems in knowing human emotions and creating the groundwork for next developments in affective computing.