# TFace **Repository Path**: mirrors_Tencent/TFace ## Basic Information - **Project Name**: TFace - **Description**: TFace: A trusty face recognition research platform developed by Tencent Youtu Lab - **Primary Language**: Python - **License**: LGPL-3.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2021-05-27 - **Last Updated**: 2025-09-27 ## Categories & Tags **Categories**: cv **Tags**: None ## 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: **`2025.02`**: `UIFace: Unleashing Inherent Model Capabilities to Enhance Intra-Class Diversity in Synthetic Face Recognition` accpted by **ICLR2025**.[[paper](https://openreview.net/forum?id=riieAeQBJm)] **`2024.12`**: `SlerpFace: Face Template Protection via Spherical Linear Interpolation` accpted by **AAAI2025**.[[paper](https://arxiv.org/pdf/2407.03043)] **`2024.03`**: `Privacy-Preserving Face Recognition Using Trainable Feature Subtraction` accpted by **CVPR2024**.[[paper](https://openaccess.thecvf.com/content/CVPR2024/papers/Mi_Privacy-Preserving_Face_Recognition_Using_Trainable_Feature_Subtraction_CVPR_2024_paper.pdf)] **`2023.10`**: `Privacy-Preserving Face Recognition Using Random Frequency Components` accpted by **ICCV2023**.[[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Mi_Privacy-Preserving_Face_Recognition_Using_Random_Frequency_Components_ICCV_2023_paper.pdf)] **`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)]