Advancements in computational emotion recognition: a synergistic approach with the emotion facial recognition dataset and RBF-GRU model architecture

Subhranil Das, Rashmi Kumari () and Raghwendra Kishore Singh
Additional contact information
Subhranil Das: University of Petroleum and Energy Studies
Rashmi Kumari: Bennett University
Raghwendra Kishore Singh: SR University

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 2, No 17, 734-749

Abstract: Abstract In this study, Radial Basis Function-Gated Recurrent Unit (RBF-GRU) has been proposed for classifying five different facial expressions, such as, Anger, Fear, Sadness, Happiness, and Neutrality. Also, we have developed Emotional Facial Recognition (EPR) dataset where that dataset contains 30,000 different images by taking consideration of Indian scenarios. For this developed model, it has been well designed for robust training and evaluating the EPR dataset. The advantage of using this model is to deliver its capabilities if any minor perturbation are present. For evaluating the performance of the model, confusion metrics parameters have been utilized which are Classification Accuracy, Precision, F1-Score, Sensitivity, and Specificity for classifying five different emotional faces. Moreover, the efficacy of the proposed RBF-GRU model has been evaluated in the existing datasets, such as CK + , FER-2013, and JAFFE. In addition to that, the effectiveness of the proposed model RBF-GRU has been compared with four existing Deep Learning algorithms where the model has achieved all the parameters at a higher note which confirms the reliability in the different emotions tasks.

Keywords: Deep learning; Emotional recognition; Radial basis function; Gated recurrent unit; Emotional pattern recognition dataset; Facial expression recognition (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-024-02645-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:16:y:2025:i:2:d:10.1007_s13198-024-02645-9

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-024-02645-9

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().