article

Facial Expression Recognition Influenced by Human Aging

Authors:

Guo Dong Guo,

Rui Guo,

Xin LiAuthors Info & Claims

IEEE Transactions on Affective Computing, Volume 4, Issue 3

Pages 291 - 298

https://doi.org/10.1109/T-AFFC.2013.13

Published: 01 July 2013 Publication History

Abstract

Facial expression recognition (FER) is an active research topic in computer vision. However, there is no study yet to discover whether FER is affected by human aging, from a computational perspective. We perform a computational study of FER within and across age groups and compare the FER accuracies. Two databases from the psychology society are introduced to the computer vision community and used for our study. We found that the FER is influenced significantly by human aging, and we analyze the influence and interpret it from a computational viewpoint. Next, we propose some schemes to reduce the influence of aging on FER and evaluate the effectiveness in dealing with lifespan FER.

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Witherow MSamad MDiawara NBar HIftekharuddin K(2024)Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark FeaturesIEEE Transactions on Affective Computing10.1109/TAFFC.2023.329707515:3(847-858)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TAFFC.2023.3297075
Park JCho S(2024)Age-Unbiased Facial Emotion Recognition with Regularizing Self-Attention Value VectorIntelligent Data Engineering and Automated Learning – IDEAL 202410.1007/978-3-031-77731-8_42(472-480)Online publication date: 19-Nov-2024
https://dl.acm.org/doi/10.1007/978-3-031-77731-8_42
Ghazouani H(2023)Challenges and Emerging Trends for Machine Reading of the Mind from Facial ExpressionsSN Computer Science10.1007/s42979-023-02447-z5:1Online publication date: 15-Dec-2023
https://dl.acm.org/doi/10.1007/s42979-023-02447-z
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Facial Expression Recognition Influenced by Human Aging
1. Computing methodologies
  1. Artificial intelligence

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cover image IEEE Transactions on Affective Computing

IEEE Transactions on Affective Computing Volume 4, Issue 3

July 2013

84 pages

ISSN:1949-3045

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 July 2013

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Witherow MSamad MDiawara NBar HIftekharuddin K(2024)Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark FeaturesIEEE Transactions on Affective Computing10.1109/TAFFC.2023.329707515:3(847-858)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TAFFC.2023.3297075
Park JCho S(2024)Age-Unbiased Facial Emotion Recognition with Regularizing Self-Attention Value VectorIntelligent Data Engineering and Automated Learning – IDEAL 202410.1007/978-3-031-77731-8_42(472-480)Online publication date: 19-Nov-2024
https://dl.acm.org/doi/10.1007/978-3-031-77731-8_42
Ghazouani H(2023)Challenges and Emerging Trends for Machine Reading of the Mind from Facial ExpressionsSN Computer Science10.1007/s42979-023-02447-z5:1Online publication date: 15-Dec-2023
https://dl.acm.org/doi/10.1007/s42979-023-02447-z
Alwidian SKühn TSousa V(2022)Towards extending the goal-oriented requirements language with emotion-oriented goals to support socio-technical systemsProceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings10.1145/3550356.3561547(306-311)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1145/3550356.3561547
Al-Garaawi NMorris TCootes T(2022)Fully automated age-weighted expression classification using real and apparent agePattern Analysis & Applications10.1007/s10044-021-01044-125:2(451-466)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s10044-021-01044-1
Pei WDibeklioğlu HBaltrušaitis TTax D(2020)Attended End-to-End Architecture for Age Estimation From Facial Expression VideosIEEE Transactions on Image Processing10.1109/TIP.2019.294828829(1972-1984)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1109/TIP.2019.2948288
Caroppo ALeone ASiciliano P(2020)Comparison Between Deep Learning Models and Traditional Machine Learning Approaches for Facial Expression Recognition in Ageing AdultsJournal of Computer Science and Technology10.1007/s11390-020-9665-435:5(1127-1146)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1007/s11390-020-9665-4
Vinola C. Vimala Devi K. (2019)Smile intensity recognition in real time videosMultimedia Tools and Applications10.1007/s11042-018-6890-878:11(15033-15052)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s11042-018-6890-8
Yang HLin BChang KChen C(2018)Joint Estimation of Age and Expression by Combining Scattering and Convolutional NetworksACM Transactions on Multimedia Computing, Communications, and Applications10.1145/315211814:1(1-18)Online publication date: 4-Jan-2018
https://dl.acm.org/doi/10.1145/3152118
Sun YYe QZhu RWen G(2018)Cognitive Gravity Model Based Semi-Supervised Dimension ReductionNeural Processing Letters10.1007/s11063-017-9648-947:1(253-276)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11063-017-9648-9
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Expression-invariant face recognition by facial expression transformations

Facial expression recognition with Convolutional Neural Networks

Pose-Robust Facial Expression Recognition Using View-Based 2D + 3D AAM

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