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Facial age estimation by nonlinear aging pattern subspace

Published: 26 October 2008 Publication History

Abstract

Human age estimation by face images is an interesting yet challenging research topic emerging in recent years. This paper extends our previous work on facial age estimation (a linear method named AGES). In order to match the nonlinear nature of the human aging progress, a new algorithm named KAGES is proposed based on a nonlinear subspace trained on the aging patterns, which are defined as sequences of individual face images sorted in time order. Both the training and test (age estimation) processes of KAGES rely on a probabilistic model of KPCA. In the experimental results, the performance of KAGES is not only better than all the compared algorithms, but also better than the human observers in age estimation. The results are sensitive to parameter choice however, and future research challenges are identified.

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  1. Facial age estimation by nonlinear aging pattern subspace

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    cover image ACM Conferences
    MM '08: Proceedings of the 16th ACM international conference on Multimedia
    October 2008
    1206 pages
    ISBN:9781605583037
    DOI:10.1145/1459359
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    New York, NY, United States

    Publication History

    Published: 26 October 2008

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    Author Tags

    1. age estimation
    2. face image

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    MM08
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    MM08: ACM Multimedia Conference 2008
    October 26 - 31, 2008
    British Columbia, Vancouver, Canada

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

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    • (2022)Facial Age Estimation Using Machine Learning Techniques: An OverviewBig Data and Cognitive Computing10.3390/bdcc60401286:4(128)Online publication date: 26-Oct-2022
    • (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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    • (2019)Ordinal Deep Learning for Facial Age EstimationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2017.278270929:2(486-501)Online publication date: 1-Feb-2019
    • (2019)Learning from Longitudinal Face Demonstration—Where Tractable Deep Modeling Meets Inverse Reinforcement LearningInternational Journal of Computer Vision10.1007/s11263-019-01165-5127:6-7(957-971)Online publication date: 1-Jun-2019
    • (2018)Expression-Invariant Age Estimation Using Structured LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2017.267973940:2(365-375)Online publication date: 1-Feb-2018
    • (2018)Age Estimation via Pose-Invariant 3D Face Alignment Feature in 3 Streams of CNNAdvances in Multimedia Information Processing – PCM 201710.1007/978-3-319-77380-3_17(172-183)Online publication date: 10-May-2018
    • (2017)Multifeature Anisotropic Orthogonal Gaussian Process for Automatic Age EstimationACM Transactions on Intelligent Systems and Technology10.1145/30903119:1(1-15)Online publication date: 4-Sep-2017
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