short-paper

Facial Age Estimation Using Robust Label Distribution

Authors:

Joni-Kristian Kämäräinen,

Zhaoxiang ZhangAuthors Info & Claims

MM '16: Proceedings of the 24th ACM international conference on Multimedia

Pages 77 - 81

https://doi.org/10.1145/2964284.2967186

Published: 01 October 2016 Publication History

Abstract

Facial age estimation, to predict the persons' exact ages given facial images, usually encounters the data sparsity problem due to the difficulties in data annotation. To mitigate the suffering from sparse data, a recent label distribution learning (LDL) algorithm attempts to embed label correlation into a classification based framework. However, the conventional label distribution learning framework only considers correlations across the neighbouring variables (ages), which omits the intrinsic complexity of age classes during different ageing periods (age groups). In the light of this, we introduce a novel concept of robust label distribution for scalar-valued labels, which is designed to encode the age scalars into label distribution matrices, i.e. two-dimensional Gaussian distributions along age classes and age groups respectively. Overcoming the limitations of conventional hard group boundaries in age grouping and capturing intrinsic inter-group dependency, our framework achieves robust and competitive performance over the conventional algorithms on two popular benchmarks for human age estimation.

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Cited By

Lu YLi WLi HJia X(2023)Ranking-preserved generative label enhancementMachine Learning10.1007/s10994-023-06388-9112:12(4693-4721)Online publication date: 20-Sep-2023
https://doi.org/10.1007/s10994-023-06388-9
Yan CMeng LLi LZhang JWang ZYin JZhang JSun YZheng B(2022)Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attentionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/347281018:1s(1-18)Online publication date: 25-Jan-2022
https://dl.acm.org/doi/10.1145/3472810
Luo JWang YOu YHe BLi B(2021)Neighbor-Based Label Distribution Learning to Model Label Ambiguity for Aerial Scene ClassificationRemote Sensing10.3390/rs1304075513:4(755)Online publication date: 18-Feb-2021
https://doi.org/10.3390/rs13040755
Show More Cited By

Index Terms

Facial Age Estimation Using Robust Label Distribution
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision representations
        Image representations
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression

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Information & Contributors

Information

Published In

cover image ACM Conferences

MM '16: Proceedings of the 24th ACM international conference on Multimedia

October 2016

1542 pages

ISBN:9781450336031

DOI:10.1145/2964284

General Chairs:
Alan Hanjalic
Delft University of Technology
,
Cees Snoek
Qualcomm Research Netherlands / University of Amsterdam
,
Marcel Worring
University of Amsterdam
,
Moderator:
Dick Bulterman
CWI / VU University Amsterdam
,
Program Chairs:
Benoit Huet
EURECOM
,
Aisling Kelliher
Virginia Tech
,
Yiannis Kompatsiaris
CERTH-ITI
,
Jin Li
Microsoft

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 01 October 2016

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Suomen Akatemia

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MM '16

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MM '16: ACM Multimedia Conference

October 15 - 19, 2016

Amsterdam, The Netherlands

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MM '16 Paper Acceptance Rate 52 of 237 submissions, 22%;

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

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Cited By

Lu YLi WLi HJia X(2023)Ranking-preserved generative label enhancementMachine Learning10.1007/s10994-023-06388-9112:12(4693-4721)Online publication date: 20-Sep-2023
https://doi.org/10.1007/s10994-023-06388-9
Yan CMeng LLi LZhang JWang ZYin JZhang JSun YZheng B(2022)Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attentionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/347281018:1s(1-18)Online publication date: 25-Jan-2022
https://dl.acm.org/doi/10.1145/3472810
Luo JWang YOu YHe BLi B(2021)Neighbor-Based Label Distribution Learning to Model Label Ambiguity for Aerial Scene ClassificationRemote Sensing10.3390/rs1304075513:4(755)Online publication date: 18-Feb-2021
https://doi.org/10.3390/rs13040755
Meng LYan CLi JYin JLiu WXie HLi LWen Chen CCucchiara RHua XQi GRicci EZhang ZZimmermann R(2020)Multi-Features Fusion and Decomposition for Age-Invariant Face RecognitionProceedings of the 28th ACM International Conference on Multimedia10.1145/3394171.3413499(3146-3154)Online publication date: 12-Oct-2020
https://dl.acm.org/doi/10.1145/3394171.3413499
Qian YChen KYang DKamarainen J(2019)Beyond Wisdom of Crowds: Deep Uncertainty Coding for Apparent Age Estimation2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC)10.1109/ICIVC47709.2019.8981088(658-662)Online publication date: Jul-2019
https://doi.org/10.1109/ICIVC47709.2019.8981088
Yang JChen LZhang LSun XShe DLu SCheng MBoll SMu Lee KLuo JZhu WByun HWen Chen CLienhart RMei T(2018)Historical Context-based Style Classification of Painting Images via Label Distribution LearningProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3240593(1154-1162)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.1145/3240508.3240593
Li KXing JHu WMaybank S(2017)D2CPattern Recognition10.1016/j.patcog.2017.01.00766:C(95-105)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.patcog.2017.01.007

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