Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS Face Recognition

TL;DR

This paper introduces a joint feature distribution alignment learning method for heterogeneous face recognition that improves cross-domain matching performance while preserving accuracy in the visible spectrum domain.

Contribution

It proposes a novel joint learning approach using knowledge distillation to enhance HFR performance without degrading VIS domain accuracy.

Findings

Significantly better HFR performance than fine-tuning methods.

Maintains high VIS domain accuracy while improving NIR-VIS matching.

Achieves comparable HFR results with less VIS performance degradation.

Abstract

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.