Abstract
Introduction; Emotion recognition is a transformative technology that enhances human-computer interaction by enabling systems to interpret and respond to human emotions effectively.
Objective; This paper investigates the current landscape of emotion recognition technologies, emphasizing the diverse sources of emotional data, including facial expressions, voice, physiological signals, and textual content.
Method; We explore the methodologies and algorithms employed for emotion classification, ranging from traditional machine learning techniques to advanced deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Result; This study provides a comparative analysis of various emotion recognition approaches, evaluating their accuracy, robustness, and computational efficiency.
Conclusion; This paper contributes to the ongoing discourse on emotion recognition by offering a comprehensive overview of current trends, challenges, and opportunities for advancing the field.
References
1. Calvo RA, D’Mello S, Gratch JM, Kappas A. The Oxford handbook of affective computing. Oxford University Press; 2015.
2. Daily SB, James MT, Cherry D, Porter III JJ, Darnell SS, Isaac J, et al. Affective computing: historical foundations, current applications, and future trends. Emot Affect Hum factors human-computer Interact. 2017;213–31.
3. Richardson S. Affective computing in the modern workplace. Bus Inf Rev. 2020;37(2):78–85.
4. Ekman P, Friesen W V. Facial action coding system. Environ Psychol Nonverbal Behav. 1978;
5. Schuller B, Batliner A. Computational paralinguistics: emotion, affect and personality in speech and language processing. John Wiley & Sons; 2013.
6. Zeng F, Hu Z, Chen R, Yang Z. Determinants of online service satisfaction and their impacts on behavioural intentions. Total Qual Manag. 2009;20(9):953–69.
7. Scherer KR. What are emotions? And how can they be measured? Soc Sci Inf. 2005;44(4):695–729.
8. Calvo RA, D’Mello S. Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Trans Affect Comput. 2010;1(1):18–37.
9. Wang Y, Song W, Tao W, Liotta A, Yang D, Li X, et al. A systematic review on affective computing: Emotion models, databases, and recent advances. Inf Fusion. 2022;83:19–52.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2025 Anamika Kumari, Smita Pallavi (Author)