Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles

TL;DR

This paper introduces the Prepended Domain Transformer (PDT), a simple yet effective neural network block added to pre-trained face recognition models, enabling high-performance heterogeneous face recognition with minimal paired data and broad applicability.

Contribution

The paper proposes a novel PDT block that can be added to any pre-trained face recognition model, allowing effective cross-domain face matching with minimal retraining and data.

Findings

Achieves state-of-the-art results on multiple HFR benchmarks.

Requires only a few paired samples for retraining the PDT block.

Compatible with various pre-trained face recognition architectures.

Abstract

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. 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. The source code and protocols will be made available publicly.