  Authentication is a task aiming to confirm the truth between data instances
and personal identities. Typical authentication applications include face
recognition, person re-identification, authentication based on mobile devices
and so on. The recently-emerging data-driven authentication process may
encounter undesired biases, i.e., the models are often trained in one domain
(e.g., for people wearing spring outfits) while required to apply in other
domains (e.g., they change the clothes to summer outfits). To address this
issue, we propose a novel two-stage method that disentangles the class/identity
from domain-differences, and we consider multiple types of domain-difference.
In the first stage, we learn disentangled representations by a one-versus-rest
disentangle learning (OVRDL) mechanism. In the second stage, we improve the
disentanglement by an additive adversarial learning (AAL) mechanism. Moreover,
we discuss the necessity to avoid a learning dilemma due to disentangling
causally related types of domain-difference. Comprehensive evaluation results
demonstrate the effectiveness and superiority of the proposed method.
