Deep learning based efficient emotion recognition technique for facial images

Naveen Kumari () and Rekha Bhatia ()
Additional contact information
Naveen Kumari: Punjabi University
Rekha Bhatia: Punjabi University

International Journal of System Assurance Engineering and Management, 2023, vol. 14, issue 4, No 20, 1436 pages

Abstract: Abstract Facial Emotion/Expression Recognition (FER) is the key technology which is responsible for analyzing facial emotions from digital images in order to reveal information on the emotional state of a person. It is one of the trending research areas in human computer interaction (HCI). This paper is supposed to provide a recent outlook in this arena by combining saliency map and deep learning approaches. As the usage of deep learning-based approaches in computer vision has revolutionized the way such problems are addressed, deep networks like convolutional neural networks (ConvNet) have become the standard design for image recognition problems. The learning through ConvNet needs a large number of images. Apart from the features of interest, most of the time these image data also carry a bunch of nonessential things, which add to the noise in our dataset. Saliency Maps accomplish this task of fixing critical pixels while ignoring the remaining image background. The present paper divides the whole process of FER into three steps: in the first step, image saliency is detected using a saliency map to focus on important regions of interest from the input dataset. In the second step, data augmentation is applied to balance all the emotion images datasets used in testing. In the third step, a deep convolutional neural network is trained using a modified adaptive moment estimation optimizer (M-Adam) to recognize the facial emotions. After this, the proposed technique is tested on the Japanese female facial expression (JAFFE), the extended Cohn-Kanade (CK+), and the face expression recognition plus dataset (FER+) benchmark datasets. The maximum accuracies achieved with the proposed technique are 97%, 99.8%, and 82.7% for the JAFFE, CK+, and FER + datasets, respectively.

Keywords: Emotions; Saliency Map; Data augmentation; M-Adam optimizer; Deep convolutional neural network (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s13198-023-01945-w 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:14:y:2023:i:4:d:10.1007_s13198-023-01945-w

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

DOI: 10.1007/s13198-023-01945-w

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 ().