DBLFace: Domain-Based Labels for NIR-VIS Heterogeneous Face Recognition

TL;DR

DBLFace introduces a domain-based label learning approach for NIR-VIS face recognition, reducing intra-class variation and data imbalance, leading to significant performance improvements and state-of-the-art results.

Contribution

The paper proposes a novel domain-based label method and specialized loss functions for NIR-VIS face recognition, addressing overfitting and data imbalance issues.

Findings

6.7% improvement in rank-1 accuracy on EDGE20

State-of-the-art performance on CASIA NIR-VIS 2.0

Effective reduction of intra-class variation

Abstract

Deep learning-based domain-invariant feature learning methods are advancing in near-infrared and visible (NIR-VIS) heterogeneous face recognition. However, these methods are prone to overfitting due to the large intra-class variation and the lack of NIR images for training. In this paper, we introduce Domain-Based Label Face (DBLFace), a learning approach based on the assumption that a subject is not represented by a single label but by a set of labels. Each label represents images of a specific domain. In particular, a set of two labels per subject, one for the NIR images and one for the VIS images, are used for training a NIR-VIS face recognition model. The classification of images into different domains reduces the intra-class variation and lessens the negative impact of data imbalance in training. To train a network with sets of labels, we introduce a domain-based angular margin loss and a maximum angular loss to maintain the inter-class discrepancy and to enforce the close relationship of labels in a set. Quantitative experiments confirm that DBLFace significantly improves the rank-1 identification rate by 6.7% on the EDGE20 dataset and achieves state-of-the-art performance on the CASIA NIR-VIS 2.0 dataset.