Emotion Recognition of Humans using modern technology of AI: A Survey

This comprehensive investigation and evaluation of the subject matter of emotion recognition is presented, focusing on the broader implications for society at large. The study utilizes a thorough analysis of scholarly literature and practical observations to establish a theoretical framework that facilitates comprehension of the topic under investigation. The findings have considerable implications for future investigations and pragmatic applications. Emotion recognition holds immense importance in diverse domains such as human-computer interaction and healthcare. The analysis of techniques used for emotional recognition includes facial expression assessment, speech patterns analysis, physiological signal interpretation, music perception, and written expression evaluation. The study presents a comprehensive overview of research methodologies commonly used in emotion recognition, discussing datasets, feature extraction techniques, and classification algorithms. The analysis of challenges and limitations pertaining to emotion recognition systems, including privacy concerns, is also discussed. Performance evaluation is analyzed through various methods, including machine assessment and self-report. The significance of continued investigation within the domains encompassing data integration across diverse modalities, the creation of robust classification algorithms, and the exploration of the intricate connection between the brain and affective states is underscored.