Research map / 3D vision / Face and shape
Pose-Aware Face Recognition in the Wild
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use pose-specific models with pose fusion instead of a single frontal model | VGGNet face recognition · IJB-A | TAR @FAR=0.01 | Helped |
| Add co-training between pose-aware models (ablation) | AlexNet PAMs · IJB-A | TAR @FAR=0.01: 0.494 -> 0.612 | Helped |
| Add co-training between pose-aware models (ablation) | AlexNet PAMs · JANUS CS2 | TAR @FAR=0.01: 0.649 -> 0.792 | Helped |
| Apply PCA to PAM features (ablation) | PAMs · IJB-A | TAR @FAR=0.01: 0.612 -> 0.666; 0.701 -> 0.768 | Helped |
| Apply Power Transformation after PCA (ablation) | PAMs · IJB-A | TAR @FAR=0.01: 0.666 -> 0.756; 0.768 -> 0.826 | Helped |
| Use only frontalized PAMout-0 rather than fusing pose-specific models (ablation) | VGGNet PAMs · IJB-A | TAR @FAR=0.01: 0.733±0.018 -> 0.826±0.018 | Helped |
| Use pose-specific models with pose fusion instead of a single frontal model | PAMs · PIPA face recognition subset | Recognition accuracy: 47.97% -> 57.65% | Helped |
Closest papers by meaning
- Towards Pose Invariant Face Recognition in the Wild (CVPR 2018 · similarity 0.89)
- Towards Universal Representation Learning for Deep Face Recognition (CVPR 2020 · similarity 0.87)
- Adversarial Pose Regression Network for Pose-Invariant Face Recognitions (AAAI 2021 · similarity 0.86)
- Dual Pose-invariant Embeddings: Learning Category and Object-specific Discriminative Representations for Recognition and Retrieval (CVPR 2024 · similarity 0.84)
- Referring Human Pose and Mask Estimation In the Wild (NeurIPS 2024 · similarity 0.84)
- HyperFace: Generating Synthetic Face Recognition Datasets by Exploring Face Embedding Hypersphere (ICLR 2025 · similarity 0.84)
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