# VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition

arXiv: https://arxiv.org/abs/1910.04985 · HTML: https://mlsanity.com/paper/1910.04985/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

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

- [WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2103.04098/) (2021)
- [MixFaceNets: Extremely Efficient Face Recognition Networks](https://mlsanity.com/paper/2107.13046/) (2021)
- [PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation](https://mlsanity.com/paper/2108.10710/) (2021)
- [EdgeFace: Efficient Face Recognition Model for Edge Devices](https://mlsanity.com/paper/2307.01838/) (2023)
- [Teacher-Student Architecture for Knowledge Distillation: A Survey](https://mlsanity.com/paper/2308.04268/) (2023)
- [Teacher-Student Architecture for Knowledge Learning: A Survey](https://mlsanity.com/paper/2210.17332/) (2022)
- [QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization](https://mlsanity.com/paper/2206.10526/) (2022)
- [ezDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipeline](https://mlsanity.com/paper/2212.05428/) (2022)
- [Deep learning with self-supervision and uncertainty regularization to count fish in underwater images](https://mlsanity.com/paper/2104.14964/) (2021)
- [SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data](https://mlsanity.com/paper/2308.14852/) (2023)
- [UADB: Unsupervised Anomaly Detection Booster](https://mlsanity.com/paper/2306.01997/) (2023)
- [AdaDistill: Adaptive Knowledge Distillation for Deep Face Recognition](https://mlsanity.com/paper/2407.01332/) (2024)
- [Towards Explaining Demographic Bias through the Eyes of Face Recognition Models](https://mlsanity.com/paper/2208.13400/) (2022)
- [EFaR 2023: Efficient Face Recognition Competition](https://mlsanity.com/paper/2308.04168/) (2023)
- [xEdgeFace: Efficient Cross-Spectral Face Recognition for Edge Devices](https://mlsanity.com/paper/2504.19646/) (2025)
- [What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias](https://mlsanity.com/paper/2410.08407/) (2024)
- [Deep Network Pruning: A Comparative Study on CNNs in Face Recognition](https://mlsanity.com/paper/2405.18302/) (2024)
- [FaceLiVTv2: An Improved Hybrid Architecture for Efficient Mobile Face Recognition](https://mlsanity.com/paper/2604.09127/) (2026)
- [Performance analysis of facial recognition: A critical review through glass factor](https://mlsanity.com/paper/2104.01536/) (2021)
- [PAMI: partition input and aggregate outputs for model interpretation](https://mlsanity.com/paper/2302.03318/) (2023)
- [SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning](https://mlsanity.com/paper/2307.10697/) (2023)
- [SymFace: Additional Facial Symmetry Loss for Deep Face Recognition](https://mlsanity.com/paper/2409.11816/) (2024)
- [IDSelect: A RL-Based Cost-Aware Selection Agent for Video-based Multi-Modal Person Recognition](https://mlsanity.com/paper/2602.18990/) (2026)
- [Lightweight Cross-Spectral Face Recognition via Contrastive Alignment and Distillation](https://mlsanity.com/paper/2605.04769/) (2026)
- [Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches](https://mlsanity.com/paper/2202.10320/) (2022)
- [Lightweight Face Recognition: An Improved MobileFaceNet Model](https://mlsanity.com/paper/2311.15326/) (2023)
- [Ef-QuantFace: Streamlined Face Recognition with Small Data and Low-Bit Precision](https://mlsanity.com/paper/2402.18163/) (2024)
- [Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition](https://mlsanity.com/paper/2409.06371/) (2024)
- [From Detection to Correction: Backdoor-Resilient Face Recognition via Vision-Language Trigger Detection and Noise-Based Neutralization](https://mlsanity.com/paper/2508.05409/) (2025)
- [Sustainable Face Recognition on Low-Power Devices with VQ-VAE Embeddings](https://mlsanity.com/paper/2606.15355/) (2026)

## Abstract (arXiv)

To improve the discriminative and generalization ability of lightweight network for face recognition, we propose an efficient variable group convolutional network called VarGFaceNet. Variable group convolution is introduced by VarGNet to solve the conflict between small computational cost and the unbalance of computational intensity inside a block. We employ variable group convolution to design our network which can support large scale face identification while reduce computational cost and parameters. Specifically, we use a head setting to reserve essential information at the start of the network and propose a particular embedding setting to reduce parameters of fully-connected layer for embedding. To enhance interpretation ability, we employ an equivalence of angular distillation loss to guide our lightweight network and we apply recursive knowledge distillation to relieve the discrepancy between the teacher model and the student model. The champion of deepglint-light track of LFR (2019) challenge demonstrates the effectiveness of our model and approach. Implementation of VarGFaceNet will be released at https://github.com/zma-c-137/VarGFaceNet soon.