Hybrid-Deep Learning Model for Emotion Recognition Using Facial Expressions

Garima Verma () and Hemraj Verma ()
Additional contact information
Garima Verma: DIT University
Hemraj Verma: DIT University

The Review of Socionetwork Strategies, 2020, vol. 14, issue 2, 171-180

Abstract: Abstract Humans use facial expressions as a tool to show emotional states. Facial expression recognition remains an interesting and challenging area of research in computer vision. An improved deep learning approach using a convolution neural network (CNN) is proposed in this paper to predict emotions by analysing facial expressions contained in an image. The model developed in this work consists of one CNN to analyse the primary emotion of the image as being happy or sad and a second CNN to predict the secondary emotion of the image. The proposed model was trained on the FER2013 and Japanese female facial expression (JAFFE) datasets with results suggesting its capability of predicting emotions from facial expressions better than existing state-of-the-art approaches.

Keywords: Facial expression; Emotion; Deep learning; Convolution neural network (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s12626-020-00061-6 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:trosos:v:14:y:2020:i:2:d:10.1007_s12626-020-00061-6

Ordering information: This journal article can be ordered from
https://www.springer ... ystems/journal/12626

DOI: 10.1007/s12626-020-00061-6

Access Statistics for this article

The Review of Socionetwork Strategies is currently edited by Katsutoshi Yada, Yasuharu Ukai and Marshall Van Alstyne

More articles in The Review of Socionetwork Strategies from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().