Although significant advances have been made in face recognition (FR), FR in
unconstrained environments remains challenging due to the domain gap between
the semi-constrained training datasets and unconstrained testing scenarios. To
address this problem, we propose a controllable face synthesis model (CFSM)
that can mimic the distribution of target datasets in a style latent space.
CFSM learns a linear subspace with orthogonal bases in the style latent space
with precise control over the diversity and degree of synthesis. Furthermore,
the pre-trained synthesis model can be guided by the FR model, making the
resulting images more beneficial for FR model training. Besides, target dataset
distributions are characterized by the learned orthogonal bases, which can be
utilized to measure the distributional similarity among face datasets. Our
approach yields significant performance gains on unconstrained benchmarks, such
as IJB-B, IJB-C, TinyFace and IJB-S (+5.76% Rank1).

提出了一种可控人脸合成模型，通过在样式潜空间中模拟目标数据集的分布，实现在非受限环境下的面部识别（FR）任务，并可指导合成图像的 FR 模型训练，获得了在 IJB-B、IJB-C、TinyFace 和 IJB-S 等非受限测试基准上的显著性能提升（+5.76% Rank1）