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https://github.com/Tencent/TFace

A trusty face analysis research platform developed by Tencent Youtu Lab
https://github.com/Tencent/TFace

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A trusty face analysis research platform developed by Tencent Youtu Lab

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README 

        



## Introduction



TFace: A trusty face analysis research platform developed by Tencent Youtu Lab. It provides a high-performance distributed training framework and releases our efficient methods implementations.

Some of the algorithms are self-developed, and we believe the released codes benefits researchers to follow.



This project consists of several modules: **Face Recognition**, **Face Security**, **Face Quality** and **Facial Attribute**.



### Face Recognition

This module implements various state-of-art algorithms for face recognition.



#### Paper List:

**`2024.03`**: `Privacy-Preserving Face Recognition Using Trainable Feature Subtraction` accpted by **CVPR2024**.

[[paper](https://arxiv.org/abs/2403.12457)]



**`2023.10`**: `Privacy-Preserving Face Recognition Using Random Frequency Components` accpted by **ICCV2023**.

[[paper](https://arxiv.org/abs/2308.10461)]



**`2022.9`**: `Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain` accepted by **ECCV2022**. 

[[paper](https://arxiv.org/abs/2207.07316)]



**`2022.9`**: `DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain` accepted by **ACMMM2022**. [[paper](https://dl.acm.org/doi/abs/10.1145/3503161.3548303)]



**`2022.6`**: `Evaluation-oriented knowledge distillation for deep face recognition` accepted by **CVPR2022**. [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Evaluation-Oriented_Knowledge_Distillation_for_Deep_Face_Recognition_CVPR_2022_paper.pdf)]



**`2021.3`**: `Consistent Instance False Positive Improves Fairness in Face Recognition` accepted by **CVPR2021**. [[paper](https://arxiv.org/abs/2106.05519)]



**`2021.3`**: `Spherical Confidence Learning for Face Recognition` accepted by **CVPR2021**. [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Spherical_Confidence_Learning_for_Face_Recognition_CVPR_2021_paper.pdf)] 



**`2020.8`**: `Improving Face Recognition from Hard Samples via Distribution Distillation Loss` accepted by **ECCV2020**. [[paper](https://arxiv.org/abs/2002.03662)]



**`2020.3`**: `Curricularface: adaptive curriculum learning loss for deep face recognition` has been accepted by **CVPR2020**. [[paper](https://arxiv.org/abs/2004.00288)]



### Face Security

This module implements various state-of-art algorithms for face security.



#### Paper List:



**`2023.09`**:  `Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition` accepted by **CVPR2023**



**`2021.12`**:  `Dual Contrastive Learning for General Face Forgery Detection` accepted by **AAAI2022**



**`2021.12`**:  `Exploiting Fine-grained Face Forgery Clues via Progressive Enhancement Learning` accepted by **AAAI2022**



**`2021.12`**:  `Delving into the Local: Dynamic Inconsistency Learning for DeepFake Video Detection` accepted by **AAAI2022**



**`2021.12`**:  `Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing` accepted by **AAAI2022**



**`2021.07`**: `Spatiotemporal Inconsistency Learning for DeepFake Video Detection`  accepted by **ACM MM2021**[[paper](https://arxiv.org/pdf/2109.01860.pdf)] [[Analysis](https://mp.weixin.qq.com/s/UMzXD4cpK4q9GXK76dbeww)]



**`2021.07`**: `Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing`  accepted by **ACM MM2021**[[paper](https://arxiv.org/abs/2108.02667)]



**`2021.07`**: `Structure Destruction and Content Combination for Face Anti-Spoofing`  accepted by **IJCB2021**[[paper](https://arxiv.org/abs/2107.10628)]



**`2021.04`**: `Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition`  accepted by **IJCAI2021**[[paper](https://www.ijcai.org/proceedings/2021/0173.pdf)]



**`2021.04`**: `Dual Reweighting Domain Generalization for Face Presentation Attack Detection`  accepted by **IJCAI2021**[[paper](https://www.ijcai.org/proceedings/2021/0120.pdf)]



**`2021.03`**: `Delving into Data: Effectively Substitute Training for Black-box Attack` accepted by **CVPR2021**. [[paper](https://arxiv.org/abs/2106.05519)]



**`2020.12`**: `Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing` accepted by **AAAI2021**. [[paper](https://arxiv.org/abs/2105.02453)]



**`2020.12`**: `Local Relation Learning for Face Forgery Detection` accepted by **AAAI2021**. [[paper](https://arxiv.org/abs/2105.02577)]



**`2020.06`**: `Face Anti-Spoofing via Disentangled Representation Learning` accepted by **ECCV2020**. [[paper](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123640630.pdf)]



### Face Quality



This module implements the SDD-FIQA algorithm for face quality.



#### Paper List:



**`2021.3`**: `SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance` accepted by **CVPR2021**. [[paper](https://arxiv.org/abs/2103.05977)]



### Facial Attribute



This module implements the M3DFEL algorithm for facial attribute.



#### Paper List:



**`2023.6`**: ` Rethinking the Learning Paradigm for Dynamic Facial Expression Recognition` accepted by **CVPR2023**. [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Rethinking_the_Learning_Paradigm_for_Dynamic_Facial_Expression_Recognition_CVPR_2023_paper.pdf)]