  Face recognition for visible light (VIS) images achieve high accuracy thanks
to the recent development of deep learning. However, heterogeneous face
recognition (HFR), which is a face matching in different domains, is still a
difficult task due to the domain discrepancy and lack of large HFR dataset.
Several methods have attempted to reduce the domain discrepancy by means of
fine-tuning, which causes significant degradation of the performance in the VIS
domain because it loses the highly discriminative VIS representation. To
overcome this problem, we propose joint feature distribution alignment learning
(JFDAL) which is a joint learning approach utilizing knowledge distillation. It
enables us to achieve high HFR performance with retaining the original
performance for the VIS domain. Extensive experiments demonstrate that our
proposed method delivers statistically significantly better performances
compared with the conventional fine-tuning approach on a public HFR dataset
Oulu-CASIA NIR&VIS and popular verification datasets in VIS domain such as FLW,
CFP, AgeDB. Furthermore, comparative experiments with existing state-of-the-art
HFR methods show that our method achieves a comparable HFR performance on the
Oulu-CASIA NIR&VIS dataset with less degradation of VIS performance.
