Rethinking the Domain Gap in Near-infrared Face Recognition

Michail Tarasiou, Jiankang Deng, Stefanos Zafeiriou; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 940-949

Heterogeneous face recognition (HFR) involves the intricate task of matching face images across the visual domains of visible (VIS) and near-infrared (NIR). While much of the existing literature on HFR identifies the domain gap as a primary challenge and directs efforts towards bridging it at either the input or feature level our work deviates from this trend. We observe that large neural networks unlike their smaller counterparts when pre-trained on large scale homogeneous VIS data demonstrate exceptional zero-shot performance in HFR suggesting that the domain gap might be less pronounced than previously believed. By approaching the HFR problem as one of low-data fine-tuning we introduce a straightforward framework: comprehensive pre-training succeeded by a regularized fine-tuning strategy that matches or surpasses the current state-of-the-art on four publicly available benchmarks. Given its simplicity and demonstrably strong performance our method could be used as a practical solution for adjusting face recognition models to HFR as well as a new baseline for future HFR research. Corresponding training and evaluation codes will be made publicly available.