YCbCr Color Space as an Effective Solution to the Problem of Low Emotion Recognition Rate of Facial Expressions In-The-Wild

Hadjer Boughanem, Haythem Ghazouani, Haythem Ghazouani, Walid Barhoumi, Walid Barhoumi

2023

Abstract

Facial expressions are natural and universal reactions for persons facing any situation, while being extremely associated with human intentions and emotional states. In this framework, Facial Emotion Recognition (FER) aims to analyze and classify a given facial image into one of several emotion states. With the recent progress in computer vision, machine learning and deep learning techniques, it is possible to effectively recognize emotions from facial images. Nevertheless, FER in a wild situation is still a challenging task due to several circumstances and various challenging factors such as heterogeneous head poses, head motion, movement blur, age, gender, occlusions, skin color, and lighting condition changes. In this work, we propose a deep learningbased facial expression recognition method, using the complementarity between deep features extracted from three pre-trained convolutional neural networks. The proposed method focuses on the quality of features offered by the YCbCr color space and demonstrates that using this color space permits to enhance the emotion recognition accuracy when dealing with images taken under challenging conditions. The obtained results, on the SFEW 2.0 dataset captured in wild environment as well as on two other facial expression benchmark which are the CK+ and the JAFFE datasets, show better performance compared to state-of-the-art methods.

Paper Citation

in Harvard Style

Boughanem H., Ghazouani H. and Barhoumi W. (2023). YCbCr Color Space as an Effective Solution to the Problem of Low Emotion Recognition Rate of Facial Expressions In-The-Wild. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 822-829. DOI: 10.5220/0011795300003417

in Bibtex Style

@conference{visapp23,
author={Hadjer Boughanem and Haythem Ghazouani and Walid Barhoumi},
title={YCbCr Color Space as an Effective Solution to the Problem of Low Emotion Recognition Rate of Facial Expressions In-The-Wild},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={822-829},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011795300003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - YCbCr Color Space as an Effective Solution to the Problem of Low Emotion Recognition Rate of Facial Expressions In-The-Wild
SN - 978-989-758-634-7
AU - Boughanem H.
AU - Ghazouani H.
AU - Barhoumi W.
PY - 2023
SP - 822
EP - 829
DO - 10.5220/0011795300003417
PB - SciTePress