Article

Learning from facial aging patterns for automatic age estimation

Authors:

Honghua DaiAuthors Info & Claims

MM '06: Proceedings of the 14th ACM international conference on Multimedia

Pages 307 - 316

https://doi.org/10.1145/1180639.1180711

Published: 23 October 2006 Publication History

Abstract

Age Specific Human-Computer Interaction (ASHCI) has vast potential applications in daily life. However, automatic age estimation technique is still underdeveloped. One of the main reasons is that the aging effects on human faces present several unique characteristics which make age estimation a challenging task that requires non-standard classification approaches. According to the speciality of the facial aging effects, this paper proposes the AGES (AGing pattErn Subspace) method for automatic age estimation. The basic idea is to model the aging pattern, which is defined as a sequence of personal aging face images, by learning a representative subspace. The proper aging pattern for an unseen face image is then determined by the projection in the subspace that can best reconstruct the face image, while the position of the face image in that aging pattern will indicate its age. The AGES method has shown encouraging performance in the comparative experiments either as an age estimator or as an age range estimator.

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Cited By

Zhang ZYan CMin QZhang YJing WHou WPan K(2024)Leverage Effective Deep Learning Searching Method for Forensic Age EstimationBioengineering10.3390/bioengineering1107067411:7(674)Online publication date: 2-Jul-2024
https://doi.org/10.3390/bioengineering11070674
Akinyemi JOnifade O(2024)An age-group ranking model for facial age estimationMachine Graphics and Vision10.22630/MGV.2024.33.1.233:1(21-45)Online publication date: 24-Oct-2024
https://doi.org/10.22630/MGV.2024.33.1.2
Ali AMarisetty ABremond F(2024)P-Age: Pexels Dataset for Robust Spatio-Temporal Apparent Age Classification2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00841(8591-8600)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00841
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Index Terms

Learning from facial aging patterns for automatic age estimation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
  2. Machine learning
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

MM '06: Proceedings of the 14th ACM international conference on Multimedia

October 2006

1072 pages

ISBN:1595934472

DOI:10.1145/1180639

General Chairs:
Klara Nahrstedt
UIUC
,
Matthew Turk
UCSB
,
Program Chairs:
Yong Rui
Microsoft Research
,
Wolfgang Klas
Universität Wien
,
Ketan Mayer-Patel
UNC

Copyright © 2006 ACM.

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Publication History

Published: 23 October 2006

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MM06: The 14th ACM International Conference on Multimedia 2006

October 23 - 27, 2006

CA, Santa Barbara, USA

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Zhang ZYan CMin QZhang YJing WHou WPan K(2024)Leverage Effective Deep Learning Searching Method for Forensic Age EstimationBioengineering10.3390/bioengineering1107067411:7(674)Online publication date: 2-Jul-2024
https://doi.org/10.3390/bioengineering11070674
Akinyemi JOnifade O(2024)An age-group ranking model for facial age estimationMachine Graphics and Vision10.22630/MGV.2024.33.1.233:1(21-45)Online publication date: 24-Oct-2024
https://doi.org/10.22630/MGV.2024.33.1.2
Ali AMarisetty ABremond F(2024)P-Age: Pexels Dataset for Robust Spatio-Temporal Apparent Age Classification2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00841(8591-8600)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00841
Yoshida CMatsushita MHigo Y(2024)Estimating the Difficulty of Programming Problems Using Fine-tuned LLM2024 IEEE/ACIS 22nd International Conference on Software Engineering Research, Management and Applications (SERA)10.1109/SERA61261.2024.10685596(28-34)Online publication date: 30-May-2024
https://doi.org/10.1109/SERA61261.2024.10685596
Beroual BSbaa S(2024)Generalized Multi_stage Feature for Deep Age Estimation from a Human Face Image13th International Conference on Information Systems and Advanced Technologies “ICISAT 2023”10.1007/978-3-031-60594-9_17(149-158)Online publication date: 31-Jul-2024
https://doi.org/10.1007/978-3-031-60594-9_17
Dong ZLi X(2023)Adaptive Age Estimation towards Imbalanced DatasetsApplied Sciences10.3390/app13181018213:18(10182)Online publication date: 11-Sep-2023
https://doi.org/10.3390/app131810182
Annalakshmi MMohamed Mansoor Roomi S(2023)Age group classification based on Bins of Gradients over Gradient Hessianspace facial imagesThe Imaging Science Journal10.1080/13682199.2023.216524770:4(228-237)Online publication date: 12-Jan-2023
https://doi.org/10.1080/13682199.2023.2165247
Zhang BBao Y(2022)Age Estimation of Faces in Videos Using Head Pose Estimation and Convolutional Neural NetworksSensors10.3390/s2211417122:11(4171)Online publication date: 31-May-2022
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ELKarazle KRaman VThen P(2022)Facial Age Estimation Using Machine Learning Techniques: An OverviewBig Data and Cognitive Computing10.3390/bdcc60401286:4(128)Online publication date: 26-Oct-2022
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Li WLu JWuerkaixi AFeng JZhou J(2022)MetaAge: Meta-Learning Personalized Age EstimatorsIEEE Transactions on Image Processing10.1109/TIP.2022.318806131(4761-4775)Online publication date: 2022
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