research-article

Multifeature Anisotropic Orthogonal Gaussian Process for Automatic Age Estimation

Authors:

Xuelong LiAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 9, Issue 1

Article No.: 2, Pages 1 - 15

https://doi.org/10.1145/3090311

Published: 04 September 2017 Publication History

Abstract

Automatic age estimation is an important yet challenging problem. It has many promising applications in social media. Of the existing age estimation algorithms, the personalized approaches are among the most popular ones. However, most person-specific approaches rely heavily on the availability of training images across different ages for a single subject, which is usually difficult to satisfy in practical application of age estimation. To address this limitation, we first propose a new model called Orthogonal Gaussian Process (OGP), which is not restricted by the number of training samples per person. In addition, without sacrifice of discriminative power, OGP is much more computationally efficient than the standard Gaussian Process. Based on OGP, we then develop an effective age estimation approach, namely anisotropic OGP (A-OGP), to further reduce the estimation error. A-OGP is based on an anisotropic noise level learning scheme that contributes to better age estimation performance. To finally optimize the performance of age estimation, we propose a multifeature A-OGP fusion framework that uses multiple features combined with a random sampling method in the feature space. Extensive experiments on several public domain face aging datasets (FG-NET, MORPH Album1, and MORPH Album 2) are conducted to demonstrate the state-of-the-art estimation accuracy of our new algorithms.

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Cited By

Chu CDonato-Woodger SKhan SNyrup RLeslie KLyn AShi TBianchi ARahimi SGrenier A(2023)Age-related bias and artificial intelligence: a scoping reviewHumanities and Social Sciences Communications10.1057/s41599-023-01999-y10:1Online publication date: 17-Aug-2023
https://doi.org/10.1057/s41599-023-01999-y
Golzardi ESheikhahmadi AAbdollahpouri A(2023)TRTCD: trust route prediction based on trusted community detectionMultimedia Tools and Applications10.1007/s11042-023-15096-482:27(41571-41607)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s11042-023-15096-4
Ribeiro de Oliveira TMoura da Silva MNepomuceno Spinasse RGiesen Ludke GRuy Soares Gaudio MIglesias Rocha Gomes GGuio Cotini LVargens DQUEIROZ SCHIMIDT MVarejao Andreao RMestria M(2021)Systematic Review of Virtual Reality Solutions Employing Artificial Intelligence MethodsProceedings of the 23rd Symposium on Virtual and Augmented Reality10.1145/3488162.3488209(42-55)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3488162.3488209
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Index Terms

Multifeature Anisotropic Orthogonal Gaussian Process for Automatic Age Estimation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Biometrics

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 9, Issue 1

Regular Papers and Special Issue: Data-driven Intelligence for Wireless Networking

January 2018

258 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3134224

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2017 ACM.

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Publication History

Published: 04 September 2017

Accepted: 01 April 2017

Revised: 01 September 2016

Received: 01 December 2015

Published in TIST Volume 9, Issue 1

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Natural Science Foundation of Guangdong Province
External Cooperation Program of BIC, the Chinese Academy of Sciences
Australian Research Council Projects

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Cited By

Chu CDonato-Woodger SKhan SNyrup RLeslie KLyn AShi TBianchi ARahimi SGrenier A(2023)Age-related bias and artificial intelligence: a scoping reviewHumanities and Social Sciences Communications10.1057/s41599-023-01999-y10:1Online publication date: 17-Aug-2023
https://doi.org/10.1057/s41599-023-01999-y
Golzardi ESheikhahmadi AAbdollahpouri A(2023)TRTCD: trust route prediction based on trusted community detectionMultimedia Tools and Applications10.1007/s11042-023-15096-482:27(41571-41607)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s11042-023-15096-4
Ribeiro de Oliveira TMoura da Silva MNepomuceno Spinasse RGiesen Ludke GRuy Soares Gaudio MIglesias Rocha Gomes GGuio Cotini LVargens DQUEIROZ SCHIMIDT MVarejao Andreao RMestria M(2021)Systematic Review of Virtual Reality Solutions Employing Artificial Intelligence MethodsProceedings of the 23rd Symposium on Virtual and Augmented Reality10.1145/3488162.3488209(42-55)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3488162.3488209
Feng PPan PZhou TChen HLuo CDemartini GZuccon GCulpepper JHuang ZTong H(2021)Zero Shot on the Cold-Start ProblemProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482312(474-483)Online publication date: 26-Oct-2021
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Ji LQin QHan BYang HDemartini GZuccon GCulpepper JHuang ZTong H(2021)Reinforcement Learning to Optimize Lifetime Value in Cold-Start RecommendationProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482292(782-791)Online publication date: 26-Oct-2021
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Zheng JMa QGu HZheng ZZhu FChin Ooi BMiao C(2021)Multi-view Denoising Graph Auto-Encoders on Heterogeneous Information Networks for Cold-start RecommendationProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467427(2338-2348)Online publication date: 14-Aug-2021
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Duan MOuyang ATan GTian Q(2021)Age Estimation Using Aging/Rejuvenation Features With Device-Edge SynergyIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.298111731:2(608-620)Online publication date: Feb-2021
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Dong MYuan FYao LXu XZhu LGupta RLiu YTang JPrakash B(2020)MAMOProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403113(688-697)Online publication date: 23-Aug-2020
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Corò FD'Angelo GVelaj Y(2019)Recommending links to maximize the influence in social networksProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367243.3367344(2195-2201)Online publication date: 10-Aug-2019
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