Heterogeneous Face Recognition (HFR) systems aim to enhance the capability of face recognition in challenging cross-modal authentication scenarios. However, the significant domain gap between the source and target modalities poses a considerable challenge for cross-domain matching. Existing literature primarily focuses on developing HFR approaches for specific pairs of face modalities, necessitating the explicit training of models for each source-target combination. In this work, we introduce a novel framework designed to train a modality-agnostic HFR method capable of handling multiple modalities during inference, all without explicit knowledge of the target modality labels. We achieve this by implementing a computationally efficient automatic routing mechanism called Switch Style Modulation Blocks (SSMB) that trains various domain expert modulators which transform the feature maps adaptively reducing the domain gap. Our proposed SSMB can be trained end-to-end and seamlessly integrated into pre-trained face recognition models, transforming them into modality-agnostic HFR models. We have performed extensive evaluations on HFR benchmark datasets to demonstrate its effectiveness. The source code and protocols will be made publicly available.

翻译：异质人脸识别系统旨在增强人脸识别在具有挑战性的跨模态认证场景中的能力。然而，源模态与目标模态之间存在显著领域差距，这对跨域匹配构成了相当大的挑战。现有文献主要集中于为特定的人脸模态对开发HFR方法，这需要为每种源-目标组合显式地训练模型。在本工作中，我们引入了一个新颖的框架，旨在训练一种模态无关的HFR方法，该方法能够在推理过程中处理多种模态，且完全无需目标模态标签的显式知识。我们通过实现一种计算高效的自动路由机制来实现这一目标，该机制称为切换风格调制块，它训练各种领域专家调制器，这些调制器自适应地转换特征图以减小领域差距。我们提出的SSMB可以进行端到端训练，并无缝集成到预训练的人脸识别模型中，从而将其转换为模态无关的HFR模型。我们在HFR基准数据集上进行了广泛的评估，以证明其有效性。源代码和协议将公开提供。