Artificial Emotional Intelligence: Conventional and deep learning approach. (February 2023)

• Record Type: Journal Article
• Authors: Kumar, Himanshu; Martin, A.
• Is Part Of: Expert systems with applications. Volume 212(2023)
• Publication Date: 2023-02
• Subjects: Artificial emotional intelligence -- Automated decision-making -- Machine learning -- Deep learning emotion detection -- Neural Network -- Facial recognition Pattern -- Facial Emotion Recognition; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118651
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A significant contribution of artificial emotional intelligence in recent years. Comparative study of conventional and deep learning techniques. Limitations and issues in existing techniques and how to overcome them. Research gap and challenges to obtain better emotion recognition performance. Research opportunities and future direction in artificial emotional intelligence. Abstract: Artificial intelligence substantially changes the global world, influencing technologies, machines, and objects in various encouraging aspects nowadays; emotion recognition is also one of them. This paper describes a significant contribution of emotion recognition by applying conventional and deep learning methodologies by focusing on limitations and demanding challenges. It also intends to explore the comparative study on recently applied machine learning and deep learning-based algorithms, which provide the best accuracy rates to recognize emotions. This Comparative study consists of different feature extractions, classifier models, and datasets that recognize the emotions within a facial image, speech, and non-verbal communication and describes their features and principles for future research work. We have shown the balancing accuracy, and efficiency of using hybrid classification techniques briefly explained in Speech emotion recognition. This review study would be more beneficial in enhancing automated decision-making services in various customer-based industries and observing patients in the health care sector, industries, public sectors, private sectors, and production firms.