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Heterogeneous Face Recognition
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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.
<span style="color:grey; opacity: 0.6">( Image credit: Pose Agnostic Cross-spectral Hallucination via Disentangling Independent Factors )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
From Modalities to Styles: Rethinking the Domain Gap in Heterogeneous Face Recognition
Heterogeneous Face Recognition Using Domain Invariant Units
Rethinking the Domain Gap in Near-infrared Face Recognition
Bridging the Gap: Heterogeneous Face Recognition with Conditional Adaptive Instance Modulation
Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles
Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement
Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS Face Recognition
Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination
Towards NIR-VIS Masked Face Recognition
A NIR-to-VIS face recognition via part adaptive and relation attention module
DBLFace: Domain-Based Labels for NIR-VIS Heterogeneous Face Recognition
DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition
Domain Private and Agnostic Feature for Modality Adaptive Face Recognition
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain Knowledge
Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition
Relational Deep Feature Learning for Heterogeneous Face Recognition
LAMP-HQ: A Large-Scale Multi-Pose High-Quality Database and Benchmark for NIR-VIS Face Recognition
Cross-Spectral Face Hallucination via Disentangling Independent Factors
Cross-Resolution Face Recognition via Prior-Aided Face Hallucination and Residual Knowledge Distillation
Dual Variational Generation for Low-Shot Heterogeneous Face Recognition
Cross-spectral Face Completion for NIR-VIS Heterogeneous Face Recognition
Disentangled Variational Representation for Heterogeneous Face Recognition
Thermal to Visible Synthesis of Face Images using Multiple Regions
Adversarial Discriminative Heterogeneous Face Recognition
Improving Heterogeneous Face Recognition with Conditional Adversarial Networks
Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition
Coupled Deep Learning for Heterogeneous Face Recognition
Sparse Graphical Representation based Discriminant Analysis for Heterogeneous Face Recognition
Frankenstein: Learning Deep Face Representations using Small Data
Graphical Representation for Heterogeneous Face Recognition
A Survey on Heterogeneous Face Recognition: Sketch, Infra-red, 3D and Low-resolution
Shared Representation Learning for Heterogeneous Face Recognition
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