Heterogeneous Face Recognition (HFR) aims to match faces across different
domains (e.g., visible to near-infrared images), which has been widely applied
in authentication and forensics scenarios. However, HFR is a challenging
problem because of the large cross-domain discrepancy, limited heterogeneous
data pairs, and large variation of facial attributes. To address these
challenges, we propose a new HFR method from the perspective of heterogeneous
data augmentation, named Face Synthesis with Identity-Attribute Disentanglement
(FSIAD). Firstly, the identity-attribute disentanglement (IAD) decouples face
images into identity-related representations and identity-unrelated
representations (called attributes), and then decreases the correlation between
identities and attributes. Secondly, we devise a face synthesis module (FSM) to
generate a large number of images with stochastic combinations of disentangled
identities and attributes for enriching the attribute diversity of synthetic
images. Both the original images and the synthetic ones are utilized to train
the HFR network for tackling the challenges and improving the performance of
HFR. Extensive experiments on five HFR databases validate that FSIAD obtains
superior performance than previous HFR approaches. Particularly, FSIAD obtains
4.8% improvement over state of the art in terms of VR@FAR=0.01% on LAMP-HQ, the
largest HFR database so far.

本文介绍一种从异构数据扩充的视角出发的新型异构人脸识别方法 —— 具有身份属性解缠的人脸合成（FSIAD），在大量合成的图像中使用随机组合的解缠特征丰富图像属性的多样性，并将合成和原始图像同时用于训练网络，以提高异构人脸识别的性能。