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Cited by 115Face recognition and analysisFace and Expression RecognitionBiometric Identification and Security

Decorrelated Adversarial Learning for Age-Invariant Face Recognition

Open access
Wang, Hao, Gong, Dihong, Li, Zhifeng, Liu, Wei·2019·2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2019.00364
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Abstract

There has been an increasing research interest in age-invariant face recognition. However, matching faces with big age gaps remains a challenging problem, primarily due to the significant discrepancy of face appearance caused by aging. To reduce such discrepancy, in this paper we present a novel algorithm to remove age-related components from features mixed with both identity and age information. Specifically, we factorize a mixed face feature into two uncorrelated components: identity-dependent component and age-dependent component, where the identity-dependent component contains information that is useful for face recognition. To implement this idea, we propose the Decorrelated Adversarial Learning (DAL) algorithm, where a Canonical Mapping Module (CMM) is introduced to find maximum correlation of the paired features generated by the backbone network, while the backbone network and the factorization module are trained to generate features reducing the correlation. Thus, the proposed model learns the decomposed features of age and identity whose correlation is significantly reduced. Simultaneously, the identity-dependent feature and the age-dependent feature are supervised by ID and age preserving signals respectively to ensure they contain the correct information. Extensive experiments have been conducted on the popular public-domain face aging datasets (FG-NET, MORPH Album 2, and CACD-VS) to demonstrate the effectiveness of the proposed approach.

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Cited by

Sorted by citations

Authors
Wang, H., Gong, D., Li, Z., & Liu, W.
Affiliations
Tencent (China) (CN)
ORCID
Venue
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2019-06
Pages
3522-3531
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2019.00364
OpenAlex
W2963767627
Cited by
115
References
4
Citations by year
2026: 5 · 2025: 7 · 2024: 18 · 2023: 16 · 2022: 26 · 2021: 25 · 2020: 12 · 2019: 6
FWCI
5.62
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion, public-domain)
  • arXiv (Cornell University) (submittedVersion, public-domain)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
  • Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
Keywords
age-invariant face recognition, facial aging, age gap, feature decomposition, correlation reduction, face recognition

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