# GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition

arXiv: https://arxiv.org/abs/2005.10497 · HTML: https://mlsanity.com/paper/2005.10497/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2020.

## Built on by (most cited first, 30 of 38)

- [ElasticFace: Elastic Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/2109.09416/) (2021)
- [Killing Two Birds with One Stone:Efficient and Robust Training of Face Recognition CNNs by Partial FC](https://mlsanity.com/paper/2203.15565/) (2022)
- [Group-aware Label Transfer for Domain Adaptive Person Re-identification](https://mlsanity.com/paper/2103.12366/) (2021)
- [WebFace260M: A Benchmark for Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2204.10149/) (2022)
- [QMagFace: Simple and Accurate Quality-Aware Face Recognition](https://mlsanity.com/paper/2111.13475/) (2021)
- [Federated Learning for Face Recognition with Gradient Correction](https://mlsanity.com/paper/2112.07246/) (2021)
- [SphereFace Revived: Unifying Hyperspherical Face Recognition](https://mlsanity.com/paper/2109.05565/) (2021)
- [Part-based Face Recognition with Vision Transformers](https://mlsanity.com/paper/2212.00057/) (2022)
- [A Quality Aware Sample-to-Sample Comparison for Face Recognition](https://mlsanity.com/paper/2306.04000/) (2023)
- [Dynamic Class Queue for Large Scale Face Recognition In the Wild](https://mlsanity.com/paper/2105.11113/) (2021)
- [A Strong Baseline for Vehicle Re-Identification](https://mlsanity.com/paper/2104.10850/) (2021)
- [UFO: Unified Feature Optimization](https://mlsanity.com/paper/2207.10341/) (2022)
- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)
- [Prototype Memory for Large-scale Face Representation Learning](https://mlsanity.com/paper/2105.02103/) (2021)
- [X2-Softmax: Margin Adaptive Loss Function for Face Recognition](https://mlsanity.com/paper/2312.05281/) (2023)
- [Identity Overlap Between Face Recognition Train/Test Data: Causing Optimistic Bias in Accuracy Measurement](https://mlsanity.com/paper/2405.09403/) (2024)
- [Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation](https://mlsanity.com/paper/2409.02555/) (2024)
- [Attention-based Partial Face Recognition](https://mlsanity.com/paper/2106.06415/) (2021)
- [Activation Template Matching Loss for Explainable Face Recognition](https://mlsanity.com/paper/2207.02179/) (2022)
- [PAMI: partition input and aggregate outputs for model interpretation](https://mlsanity.com/paper/2302.03318/) (2023)
- [Deep Learning Based Face Recognition Method using Siamese Network](https://mlsanity.com/paper/2312.14001/) (2023)
- [Autoencoder Based Face Verification System](https://mlsanity.com/paper/2312.14301/) (2023)
- [GIF: Generative Inspiration for Face Recognition at Scale](https://mlsanity.com/paper/2505.03012/) (2025)
- [Fast and Reliable Probabilistic Face Embeddings in the Wild](https://mlsanity.com/paper/2102.04075/) (2021)
- [Susceptibility to Image Resolution in Face Recognition and Trainings Strategies](https://mlsanity.com/paper/2107.03769/) (2021)
- [SubFace: Learning with Softmax Approximation for Face Recognition](https://mlsanity.com/paper/2208.11483/) (2022)
- [A Survey of Face Recognition](https://mlsanity.com/paper/2212.13038/) (2022)
- [Effective Adapter for Face Recognition in the Wild](https://mlsanity.com/paper/2312.01734/) (2023)
- [Unsupervised Deep Learning Image Verification Method](https://mlsanity.com/paper/2312.14395/) (2023)
- [Minimizing Embedding Distortion for Robust Out-of-Distribution Performance](https://mlsanity.com/paper/2409.07582/) (2024)

## Abstract (arXiv)

In the field of face recognition, a model learns to distinguish millions of face images with fewer dimensional embedding features, and such vast information may not be properly encoded in the conventional model with a single branch. We propose a novel face-recognition-specialized architecture called GroupFace that utilizes multiple group-aware representations, simultaneously, to improve the quality of the embedding feature. The proposed method provides self-distributed labels that balance the number of samples belonging to each group without additional human annotations, and learns the group-aware representations that can narrow down the search space of the target identity. We prove the effectiveness of the proposed method by showing extensive ablation studies and visualizations. All the components of the proposed method can be trained in an end-to-end manner with a marginal increase of computational complexity. Finally, the proposed method achieves the state-of-the-art results with significant improvements in 1:1 face verification and 1:N face identification tasks on the following public datasets: LFW, YTF, CALFW, CPLFW, CFP, AgeDB-30, MegaFace, IJB-B and IJB-C.