Heterogeneous Face Recognition (HFR) is a challenging issue because of the
large domain discrepancy and a lack of heterogeneous data. This paper considers
HFR as a dual generation problem, and proposes a novel Dual Variational
Generation (DVG) framework. It generates large-scale new paired heterogeneous
images with the same identity from noise, for the sake of reducing the domain
gap of HFR. Specifically, we first introduce a dual variational autoencoder to
represent a joint distribution of paired heterogeneous images. Then, in order
to ensure the identity consistency of the generated paired heterogeneous
images, we impose a distribution alignment in the latent space and a pairwise
identity preserving in the image space. Moreover, the HFR network reduces the
domain discrepancy by constraining the pairwise feature distances between the
generated paired heterogeneous images. Extensive experiments on four HFR
databases show that our method can significantly improve state-of-the-art
results. The related code is available at this https URL

本文提出一种新的双变分生成框架 (DVG)，通过生成大规模的具有相同身份的异构图像，从噪声中减少异构人脸识别 (HFR) 中领域差距，实现了身份一致性和配对特征距离的约束，有效缩小了领域差距。经过在四个 HFR 数据集上的广泛实验测试，表明我们的方法可以显著改进最先进的结果。