Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2024.047326
Journal Menu
Special lssues
Table of Content

All issues

Online First

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

Faster Region Convolutional Neural Network (FRCNN) Based Facial Emotion Recognition

J. Sheril Angel1, A. Diana Andrushia1,*, T. Mary Neebha1, Oussama Accouche2, Louai Saker2, N. Anand3
1 Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, 641114, India
2 College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait
3 Department of Civil Engineering, Karunya Institute of Technology and Sciences, Coimbatore, 641114, India
* Corresponding Author: A. Diana Andrushia. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.047326

Received 02 November 2023; Accepted 18 March 2024; Published online 17 April 2024

Abstract

Facial emotion recognition (FER) has become a focal point of research due to its widespread applications, ranging from human-computer interaction to affective computing. While traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets, recent strides in artificial intelligence and deep learning (DL) have ushered in more sophisticated approaches. The research aims to develop a FER system using a Faster Region Convolutional Neural Network (FRCNN) and design a specialized FRCNN architecture tailored for facial emotion recognition, leveraging its ability to capture spatial hierarchies within localized regions of facial features. The proposed work enhances the accuracy and efficiency of facial emotion recognition. The proposed work comprises two major key components: Inception V3-based feature extraction and FRCNN-based emotion categorization. Extensive experimentation on Kaggle datasets validates the effectiveness of the proposed strategy, showcasing the FRCNN approach’s resilience and accuracy in identifying and categorizing facial expressions. The model’s overall performance metrics are compelling, with an accuracy of 98.4%, precision of 97.2%, and recall of 96.31%. This work introduces a perceptive deep learning-based FER method, contributing to the evolving landscape of emotion recognition technologies. The high accuracy and resilience demonstrated by the FRCNN approach underscore its potential for real-world applications. This research advances the field of FER and presents a compelling case for the practicality and efficacy of deep learning models in automating the understanding of facial emotions.

Keywords

Facial emotions; FRCNN; deep learning; emotion recognition; face; CNN

  • 107

    View

  • 16

    Download

  • 0

    Like

Related articles
Copyright© 2024 Tech Science Press
© 1997-2024 TSP (Henderson, USA) unless otherwise stated

Share Link