# Decorrelated Adversarial Learning for Age-Invariant Face Recognition

arXiv: https://arxiv.org/abs/1904.04972 · HTML: https://mlsanity.com/paper/1904.04972/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

## Built on by (most cited first, 21 of 21)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [SynFace: Face Recognition with Synthetic Data](https://mlsanity.com/paper/2108.07960/) (2021)
- [WebFace260M: A Benchmark for Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2204.10149/) (2022)
- [When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework](https://mlsanity.com/paper/2103.01520/) (2021)
- [LARNet: Lie Algebra Residual Network for Face Recognition](https://mlsanity.com/paper/2103.08147/) (2021)
- [Local-Adaptive Face Recognition via Graph-based Meta-Clustering and Regularized Adaptation](https://mlsanity.com/paper/2203.14327/) (2022)
- [When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark](https://mlsanity.com/paper/2210.09835/) (2022)
- [CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition](https://mlsanity.com/paper/2310.00359/) (2023)
- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)
- [Improving Face Recognition with Large Age Gaps by Learning to Distinguish Children](https://mlsanity.com/paper/2110.11630/) (2021)
- [Learning Fair Face Representation With Progressive Cross Transformer](https://mlsanity.com/paper/2108.04983/) (2021)
- [From Age Estimation to Age-Invariant Face Recognition: Generalized Age Feature Extraction Using Order-Enhanced Contrastive Learning](https://mlsanity.com/paper/2501.01760/) (2025)
- [Synthetic Face Ageing: Evaluation, Analysis and Facilitation of Age-Robust Facial Recognition Algorithms](https://mlsanity.com/paper/2406.06932/) (2024)
- [Impact of Facial Tattoos and Paintings on Face Recognition Systems](https://mlsanity.com/paper/2103.09939/) (2021)
- [Cross-Age Contrastive Learning for Age-Invariant Face Recognition](https://mlsanity.com/paper/2312.11195/) (2023)
- [A Survey of Face Recognition](https://mlsanity.com/paper/2212.13038/) (2022)
- [Set-Based Face Recognition Beyond Disentanglement: Burstiness Suppression With Variance Vocabulary](https://mlsanity.com/paper/2304.06249/) (2023)
- [Deep Collaborative Multi-Modal Learning for Unsupervised Kinship Estimation](https://mlsanity.com/paper/2109.02804/) (2021)
- [Just Noticeable Difference Modeling for Face Recognition System](https://mlsanity.com/paper/2209.05856/) (2022)
- [Age-Invariant Face Embedding using the Wasserstein Distance](https://mlsanity.com/paper/2305.02745/) (2023)
- [DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition](https://mlsanity.com/paper/2403.08318/) (2024)

## Abstract (arXiv)

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 appearances caused by aging. To reduce such a discrepancy, in this paper we propose 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 includes 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 the maximum correlation between the paired features generated by a 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 respectively supervised by ID and age preserving signals to ensure that they both contain the correct information. Extensive experiments are conducted on popular public-domain face aging datasets (FG-NET, MORPH Album 2, and CACD-VS) to demonstrate the effectiveness of the proposed approach.