Heterogeneous Face Recognition
6 papers with code • 3 benchmarks • 2 datasets
Heterogeneous face recognition is the task of matching face images acquired from different sources (i.e., different sensors or different wavelengths) for identification or verification.
( Image credit: Pose Agnostic Cross-spectral Hallucination via Disentangling Independent Factors )
Benchmarks
These leaderboards are used to track progress in Heterogeneous Face Recognition
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DFW
MCXFACE
Latest papers with no code
Rethinking the Domain Gap in Near-infrared Face Recognition
Heterogeneous face recognition (HFR) involves the intricate task of matching face images across the visual domains of visible (VIS) and near-infrared (NIR).
Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement
Secondly, we devise a face synthesis module (FSM) to generate a large number of images with stochastic combinations of disentangled identities and attributes for enriching the attribute diversity of synthetic images.
Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS Face Recognition
Furthermore, comparative experiments with existing state-of-the-art HFR methods show that our method achieves a comparable HFR performance on the Oulu-CASIA NIR&VIS dataset with less degradation of VIS performance.
Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination
In Heterogeneous Face Recognition (HFR), the objective is to match faces across two different domains such as visible and thermal.
Towards NIR-VIS Masked Face Recognition
In this paper, we aim to address the challenge of NIR-VIS masked face recognition from the perspectives of training data and training method.
A NIR-to-VIS face recognition via part adaptive and relation attention module
In the face recognition application scenario, we need to process facial images captured in various conditions, such as at night by near-infrared (NIR) surveillance cameras.
DBLFace: Domain-Based Labels for NIR-VIS Heterogeneous Face Recognition
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.
Domain Private and Agnostic Feature for Modality Adaptive Face Recognition
Second, in FFM, the identity features are fused with domain features to achieve cross-modal bi-directional identity feature transformation, which, to a large extent, further disentangles the modality information and identity information.
Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition
Heterogeneous face recognition (HFR) refers to matching face images acquired from different domains with wide applications in security scenarios.
Relational Deep Feature Learning for Heterogeneous Face Recognition
Due to the lack of databases, HFR methods usually exploit the pre-trained features on a large-scale visual database that contain general facial information.
