# When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework

arXiv: https://arxiv.org/abs/2103.01520 · HTML: https://mlsanity.com/paper/2103.01520/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 61 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [Continuously Indexed Domain Adaptation](https://mlsanity.com/paper/2007.01807/) (2020)
- [Attribute-aware Face Aging with Wavelet-based Generative Adversarial Networks](https://mlsanity.com/paper/1809.06647/) (2018)
- [Learning Face Age Progression: A Pyramid Architecture of GANs](https://mlsanity.com/paper/1711.10352/) (2017)
- [Decorrelated Adversarial Learning for Age-Invariant Face Recognition](https://mlsanity.com/paper/1904.04972/) (2019)
- [Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition](https://mlsanity.com/paper/1809.00338/) (2018)
- [Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition](https://mlsanity.com/paper/1810.07599/) (2018)
- [PFA-GAN: Progressive Face Aging with Generative Adversarial Network](https://mlsanity.com/paper/2012.03459/) (2020)
- [Temporal Non-Volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition](https://mlsanity.com/paper/1703.08617/) (2017)
- [Age Progression and Regression with Spatial Attention Modules](https://mlsanity.com/paper/1903.02133/) (2019)
- [Finding Missing Children: Aging Deep Face Features](https://mlsanity.com/paper/1911.07538/) (2019)

## Built on by (most cited first, 29 of 29)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [WebFace260M: A Benchmark for Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2204.10149/) (2022)
- [Face Aging via Diffusion-based Editing](https://mlsanity.com/paper/2309.11321/) (2023)
- [Semi-supervised Multi-task Learning for Semantics and Depth](https://mlsanity.com/paper/2110.07197/) (2021)
- [Improving Continuous Sign Language Recognition with Consistency Constraints and Signer Removal](https://mlsanity.com/paper/2212.13023/) (2022)
- [Multi-Task Neural Processes](https://mlsanity.com/paper/2111.05820/) (2021)
- [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)
- [When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review](https://mlsanity.com/paper/2307.14382/) (2023)
- [Contextualizing Meta-Learning via Learning to Decompose](https://mlsanity.com/paper/2106.08112/) (2021)
- [Improving Face Recognition with Large Age Gaps by Learning to Distinguish Children](https://mlsanity.com/paper/2110.11630/) (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)
- [Prototype Memory for Large-scale Face Representation Learning](https://mlsanity.com/paper/2105.02103/) (2021)
- [AAFACE: Attribute-aware Attentional Network for Face Recognition](https://mlsanity.com/paper/2308.07243/) (2023)
- [CATFace: Cross-Attribute-Guided Transformer with Self-Attention Distillation for Low-Quality Face Recognition](https://mlsanity.com/paper/2401.03037/) (2024)
- [Synthetic Face Ageing: Evaluation, Analysis and Facilitation of Age-Robust Facial Recognition Algorithms](https://mlsanity.com/paper/2406.06932/) (2024)
- [AgeFlow: Conditional Age Progression and Regression with Normalizing Flows](https://mlsanity.com/paper/2105.07239/) (2021)
- [Causal Representation Learning for Context-Aware Face Transfer](https://mlsanity.com/paper/2110.01571/) (2021)
- [Cross-Age Contrastive Learning for Age-Invariant Face Recognition](https://mlsanity.com/paper/2312.11195/) (2023)
- [ChildPredictor: A Child Face Prediction Framework with Disentangled Learning](https://mlsanity.com/paper/2204.09962/) (2022)
- [A Survey of Face Recognition](https://mlsanity.com/paper/2212.13038/) (2022)
- [Knowledge Assembly: Semi-Supervised Multi-Task Learning from Multiple Datasets with Disjoint Labels](https://mlsanity.com/paper/2306.08839/) (2023)
- [Effective Adapter for Face Recognition in the Wild](https://mlsanity.com/paper/2312.01734/) (2023)
- [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)
- [SASMU: boost the performance of generalized recognition model using synthetic face dataset](https://mlsanity.com/paper/2306.01449/) (2023)
- [Seeing through the Mask: Multi-task Generative Mask Decoupling Face Recognition](https://mlsanity.com/paper/2311.11512/) (2023)
- [Adversarial Purification of Information Masking](https://mlsanity.com/paper/2311.15339/) (2023)
- [DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition](https://mlsanity.com/paper/2403.08318/) (2024)
- [TimeWeaver: Age-Consistent Reference-Based Face Restoration with Identity Preservation](https://mlsanity.com/paper/2603.22701/) (2026)

## Abstract (arXiv)

To minimize the effects of age variation in face recognition, previous work either extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features, called age-invariant face recognition (AIFR), or removes age variation by transforming the faces of different age groups into the same age group, called face age synthesis (FAS); however, the former lacks visual results for model interpretation while the latter suffers from artifacts compromising downstream recognition. Therefore, this paper proposes a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn age-invariant identity-related representation while achieving pleasing face synthesis. Specifically, we first decompose the mixed face feature into two uncorrelated components -- identity- and age-related feature -- through an attention mechanism, and then decorrelate these two components using multi-task training and continuous domain adaption. In contrast to the conventional one-hot encoding that achieves group-level FAS, we propose a novel identity conditional module to achieve identity-level FAS, with a weight-sharing strategy to improve the age smoothness of synthesized faces. In addition, we collect and release a large cross-age face dataset with age and gender annotations to advance the development of the AIFR and FAS. Extensive experiments on five benchmark cross-age datasets demonstrate the superior performance of our proposed MTLFace over existing state-of-the-art methods for AIFR and FAS. We further validate MTLFace on two popular general face recognition datasets, showing competitive performance for face recognition in the wild. The source code and dataset are available at~urlhttps://github.com/Hzzone/MTLFace.