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Heterogeneous Face Recognition with Prepended Domain Transformer


Heterogeneous Face Recognition (HFR) refers to matching face images captured in different domains, such as thermal to visible images (VIS), sketches to visible images, near-infrared to visible, and so on. This is particularly useful in matching visible spectrum images to images captured from other modalities. Though highly useful, HFR is challenging because of the domain gap between the source and target domain. Often, large-scale paired heterogeneous face image datasets are absent, preventing training models specifically for the heterogeneous task. In this work, we propose a surprisingly simple, yet, very effective method for matching face images across different sensing modalities.

The core idea of the proposed approach is to add a novel neural network block called Prepended Domain Transformer (PDT) in front of a pre-trained face recognition (FR) model to address the domain gap. Retraining this new block with few paired samples in a contrastive learning setup was enough to achieve state- of-the-art performance in many HFR benchmarks.


George, A. and Mohammadi, A. and Marcel, S. (2022). Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles. IEEE Transactions on Information Forensics and Security.


The PDT blocks can be retrained for several source-target combinations using the proposed general framework. The proposed approach is architecture agnostic, meaning they can be added to any pre-trained FR models. Further, the approach is modular and the new block can be trained with a minimal set of paired samples, making it much easier for practical deployment.


  • Face Recognition

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Target domain images are transformed using the proposed Prepended Domain Transformer (PDT) block. The PDT block prepended to the frozen FR model is trained in a standard Siamese setting with contrastive loss.

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