A Unified Framework of Deep Learning-Based Facial Expression Recognition System for Diversified Applications

Author(s)

Sanoar Hossain (0000-0002-1232-7487), Aliah UniversityFollow
Saiyed Umer (0000-0002-1476-041X), Aliah UniversityFollow
Vijayan K. Asari (0000-0002-3751-5492), University of DaytonFollow
Ranjeet Kumar Rout (0000-0002-1546-1702), National Institute of Technology-SrinagarFollow

Document Type

Article

Publication Date

10-2021

Publication Source

Applied Sciences-BASEL

Abstract

This work proposes a facial expression recognition system for a diversified field of appli- cations. The purpose of the proposed system is to predict the type of expressions in a human face region. The implementation of the proposed method is fragmented into three components. In the first component, from the given input image, a tree-structured part model has been applied that predicts some landmark points on the input image to detect facial regions. The detected face region was normalized to its fixed size and then down-sampled to its varying sizes such that the advantages, due to the effect of multi-resolution images, can be introduced. Then, some convolutional neural network (CNN) architectures were proposed in the second component to analyze the texture patterns in the facial regions. To enhance the proposed CNN model’s performance, some advanced techniques, such data augmentation, progressive image resizing, transfer-learning, and fine-tuning of the parameters, were employed in the third component to extract more distinctive and discriminant features for the proposed facial expression recognition system. The performance of the proposed system, due to dif- ferent CNN models, is fused to achieve better performance than the existing state-of-the-art methods and for this reason, extensive experimentation has been carried out using the Karolinska-directed emotional faces (KDEF), GENKI-4k, Cohn-Kanade (CK+), and Static Facial Expressions in the Wild (SFEW) benchmark databases. The performance has been compared with some existing methods concerning these databases, which shows that the proposed facial expression recognition system outperforms other competing methods.

ISBN/ISSN

2076-3417

Document Version

Published Version

Comments

This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.3390/app11199174

Publisher

MDPI

Volume

11

Peer Reviewed

yes

Issue

19

eCommons Citation

Hossain, Sanoar; Umer, Saiyed; Asari, Vijayan K.; and Rout, Ranjeet Kumar, "A Unified Framework of Deep Learning-Based Facial Expression Recognition System for Diversified Applications" (2021). Electrical and Computer Engineering Faculty Publications. 473.
https://ecommons.udayton.edu/ece_fac_pub/473