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~https://github.com/Hzzone/MTLFace.

本文提出了一种统一的、多任务的人脸识别框架 MTLFace，能够在处理年龄变化对人脸识别的干扰时实现年龄不变的身份相关特征提取，同时避免了传统面部年龄合成处理中的伪影和瑕疵，取得了较好的实验结果和性能表现。

当年龄不变人脸识别遇到人脸年龄合成：多任务学习框架

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

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.

本文提出了一种新的算法 ——Decorrelated Adversarial Learning (DAL)，该算法能够将包含身份信息和年龄信息的脸部特征分解成身份相关组分和年龄相关组分，并利用 ID 和 age 保留信号监督身份相关特征和年龄相关特征以确保两者都包含正确信息，实验结果表明该方法对于面部年龄差异引起的困难问题具有很好的鲁棒性。