  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.
