# MixFaceNets: Extremely Efficient Face Recognition Networks

arXiv: https://arxiv.org/abs/2107.13046 · HTML: https://mlsanity.com/paper/2107.13046/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 19 of 19)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://mlsanity.com/paper/1812.00332/) (2018)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices](https://mlsanity.com/paper/1804.07573/) (2018)
- [Partial FC: Training 10 Million Identities on a Single Machine](https://mlsanity.com/paper/2010.05222/) (2020)
- [A Light CNN for Deep Face Representation with Noisy Labels](https://mlsanity.com/paper/1511.02683/) (2015)
- [MixConv: Mixed Depthwise Convolutional Kernels](https://mlsanity.com/paper/1907.09595/) (2019)
- [Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions](https://mlsanity.com/paper/1711.08141/) (2017)
- [Self-restrained Triplet Loss for Accurate Masked Face Recognition](https://mlsanity.com/paper/2103.01716/) (2021)
- [VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition](https://mlsanity.com/paper/1910.04985/) (2019)
- [AirFace: Lightweight and Efficient Model for Face Recognition](https://mlsanity.com/paper/1907.12256/) (2019)
- [VarGNet: Variable Group Convolutional Neural Network for Efficient Embedded Computing](https://mlsanity.com/paper/1907.05653/) (2019)

## Built on by (most cited first, 30 of 36)

- [SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data](https://mlsanity.com/paper/2206.10520/) (2022)
- [Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors](https://mlsanity.com/paper/2203.06691/) (2022)
- [MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders](https://mlsanity.com/paper/2302.01843/) (2023)
- [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)
- [SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2208.07337/) (2022)
- [QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization](https://mlsanity.com/paper/2206.10526/) (2022)
- [Unsupervised Face Morphing Attack Detection via Self-paced Anomaly Detection](https://mlsanity.com/paper/2208.05787/) (2022)
- [FocusFace: Multi-task Contrastive Learning for Masked Face Recognition](https://mlsanity.com/paper/2110.14940/) (2021)
- [Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2306.15733/) (2023)
- [Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detection](https://mlsanity.com/paper/2111.04336/) (2021)
- [OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement](https://mlsanity.com/paper/2208.07841/) (2022)
- [Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection](https://mlsanity.com/paper/2306.03002/) (2023)
- [SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data](https://mlsanity.com/paper/2308.14852/) (2023)
- [Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Quality](https://mlsanity.com/paper/2208.05864/) (2022)
- [Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models](https://mlsanity.com/paper/2112.04827/) (2021)
- [Face Morphing Attack Detection Using Privacy-Aware Training Data](https://mlsanity.com/paper/2207.00899/) (2022)
- [OCFR 2022: Competition on Occluded Face Recognition From Synthetically Generated Structure-Aware Occlusions](https://mlsanity.com/paper/2208.02760/) (2022)
- [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)
- [SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning](https://mlsanity.com/paper/2504.05504/) (2025)
- [IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection](https://mlsanity.com/paper/2408.01690/) (2024)
- [SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2311.05336/) (2023)
- [Model Compression Techniques in Biometrics Applications: A Survey](https://mlsanity.com/paper/2401.10139/) (2024)
- [xEdgeFace: Efficient Cross-Spectral Face Recognition for Edge Devices](https://mlsanity.com/paper/2504.19646/) (2025)
- [Estimating Deep Learning energy consumption based on model architecture and training environment](https://mlsanity.com/paper/2307.05520/) (2023)
- [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)
- [SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning](https://mlsanity.com/paper/2307.10697/) (2023)
- [Efficient Verification-Based Face Identification](https://mlsanity.com/paper/2312.13240/) (2023)

## Abstract (arXiv)

In this paper, we present a set of extremely efficient and high throughput models for accurate face verification, MixFaceNets which are inspired by Mixed Depthwise Convolutional Kernels. Extensive experiment evaluations on Label Face in the Wild (LFW), Age-DB, MegaFace, and IARPA Janus Benchmarks IJB-B and IJB-C datasets have shown the effectiveness of our MixFaceNets for applications requiring extremely low computational complexity. Under the same level of computation complexity (< 500M FLOPs), our MixFaceNets outperform MobileFaceNets on all the evaluated datasets, achieving 99.60% accuracy on LFW, 97.05% accuracy on AgeDB-30, 93.60 TAR (at FAR1e-6) on MegaFace, 90.94 TAR (at FAR1e-4) on IJB-B and 93.08 TAR (at FAR1e-4) on IJB-C. With computational complexity between 500M and 1G FLOPs, our MixFaceNets achieved results comparable to the top-ranked models, while using significantly fewer FLOPs and less computation overhead, which proves the practical value of our proposed MixFaceNets. All training codes, pre-trained models, and training logs have been made available https://github.com/fdbtrs/mixfacenets.