Abstract
Heterogeneous Face Recognition (HFR) aims to expand the applicability of Face Recognition (FR) systems to challenging scenarios, enabling the matching of face images across different domains, such as matching thermal images to visible spectra. However, the development of HFR systems is challenging because of the significant domain gap between modalities and the lack of availability of large-scale paired multi-channel data. In this work, we leverage a pretrained face recognition model as a teacher network to learn domaininvariant network layers called Domain-Invariant Units (DIU) to reduce the domain gap. The proposed DIU can be trained effectively even with a limited amount of paired training data, in a contrastive distillation framework. This proposed approach has the potential to enhance pretrained models, making them more adaptable to a wider range of variations in data. We extensively evaluate our approach on multiple challenging benchmarks, demonstrating superior performance compared to state-of-the-art methods.
Abstract (translated)
异质面部识别（HFR）旨在将面部识别（FR）系统的应用扩展到具有挑战性的场景中，实现不同领域间面部图像的匹配，例如将热成像与可见光谱进行匹配。然而，由于模态之间的显著差异和大规模多通道数据缺乏，HFR系统的发展具有挑战性。在这项工作中，我们利用预训练的人脸识别模型作为教师网络，学习领域无关网络层，称为领域无关单元（DIU），以减少领域差距。所提出的DIU可以在训练过程中有效处理有限量的成对训练数据，并通过对比性蒸馏框架实现有效训练。这种方法具有提高预训练模型的潜力，使它们对数据中的更广泛的变异性具有更强的适应性。我们对我们的方法在多个具有挑战性的基准进行了广泛评估，证明了其在最先进的methods之上的卓越性能。
URL
https://arxiv.org/abs/2404.14343
