Search papers, labs, and topics across Lattice.
This paper introduces a lightweight heterogeneous face recognition (HFR) framework based on a hybrid CNN-Transformer architecture adapted from RGB face recognition. The method uses contrastive alignment and distillation to enable efficient end-to-end training with limited paired heterogeneous data, while preserving performance on RGB benchmarks. Experiments demonstrate state-of-the-art or competitive HFR performance with significantly reduced computational cost, making it suitable for resource-constrained devices.
Resource-strapped edge devices can now achieve state-of-the-art face recognition across different sensing modalities thanks to a new lightweight CNN-Transformer architecture.
Heterogeneous Face Recognition (HFR) aims at matching face images captured across different sensing modalities, such as thermal-to-visible or near-infrared-to-visible, enhancing the usability of face recognition systems in challenging real-world conditions. Although recent HFR methods have achieved significant improvements in performance, many rely on computationally expensive models, making them impractical for deployment on resource-limited edge devices. In this work, we introduce a lightweight yet effective HFR framework by adapting a hybrid CNN-Transformer model originally developed for RGB homogeneous face recognition. Our approach enables efficient end-to-end training with only a small amount of paired heterogeneous data, while still maintaining strong performance on standard RGB face recognition benchmarks. This makes it suitable for both homogeneous and heterogeneous settings. Comprehensive experiments on several challenging HFR and face recognition benchmarks show that our method achieves state-of-the-art or competitive performance while keeping computational requirements low.
