# QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization

arXiv: https://arxiv.org/abs/2206.10526 · HTML: https://mlsanity.com/paper/2206.10526/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 60 references that are in the map.
- First posted on arXiv 21 Jun 2022.

## Builds on (most cited first, 30 of 36)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (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)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://mlsanity.com/paper/1806.08342/) (2018)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT](https://mlsanity.com/paper/1909.05840/) (2019)
- [LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks](https://mlsanity.com/paper/1807.10029/) (2018)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [Adversarial Domain Adaptation with Domain Mixup](https://mlsanity.com/paper/1912.01805/) (2019)
- [Quantized Convolutional Neural Networks for Mobile Devices](https://mlsanity.com/paper/1512.06473/) (2015)
- [Low-bit Quantization of Neural Networks for Efficient Inference](https://mlsanity.com/paper/1902.06822/) (2019)
- [MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices](https://mlsanity.com/paper/1804.07573/) (2018)
- [ElasticFace: Elastic Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/2109.09416/) (2021)
- [MixConv: Mixed Depthwise Convolutional Kernels](https://mlsanity.com/paper/1907.09595/) (2019)
- [SynFace: Face Recognition with Synthetic Data](https://mlsanity.com/paper/2108.07960/) (2021)
- [Generative Low-bitwidth Data Free Quantization](https://mlsanity.com/paper/2003.03603/) (2020)
- [Data-Free Network Quantization With Adversarial Knowledge Distillation](https://mlsanity.com/paper/2005.04136/) (2020)
- [Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors](https://mlsanity.com/paper/2203.06691/) (2022)
- [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)

## Built on by (most cited first, 18 of 18)

- [IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models](https://mlsanity.com/paper/2308.04995/) (2023)
- [Synthetic Data for Face Recognition: Current State and Future Prospects](https://mlsanity.com/paper/2305.01021/) (2023)
- [EdgeFace: Efficient Face Recognition Model for Edge Devices](https://mlsanity.com/paper/2307.01838/) (2023)
- [Unsupervised Face Recognition using Unlabeled Synthetic Data](https://mlsanity.com/paper/2211.07371/) (2022)
- [Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition](https://mlsanity.com/paper/2305.00358/) (2023)
- [ExFaceGAN: Exploring Identity Directions in GAN's Learned Latent Space for Synthetic Identity Generation](https://mlsanity.com/paper/2307.05151/) (2023)
- [SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data](https://mlsanity.com/paper/2303.02660/) (2023)
- [Bias and Diversity in Synthetic-based Face Recognition](https://mlsanity.com/paper/2311.03970/) (2023)
- [Causality-Inspired Taxonomy for Explainable Artificial Intelligence](https://mlsanity.com/paper/2208.09500/) (2022)
- [Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognition](https://mlsanity.com/paper/2308.11840/) (2023)
- [AdaDistill: Adaptive Knowledge Distillation for Deep Face Recognition](https://mlsanity.com/paper/2407.01332/) (2024)
- [EFaR 2023: Efficient Face Recognition Competition](https://mlsanity.com/paper/2308.04168/) (2023)
- [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)
- [A Survey on Drowsiness Detection -- Modern Applications and Methods](https://mlsanity.com/paper/2408.12990/) (2024)
- [Massively Annotated Datasets for Assessment of Synthetic and Real Data in Face Recognition](https://mlsanity.com/paper/2404.15234/) (2024)
- [Fully Quantized Always-on Face Detector Considering Mobile Image Sensors](https://mlsanity.com/paper/2311.01001/) (2023)
- [Ef-QuantFace: Streamlined Face Recognition with Small Data and Low-Bit Precision](https://mlsanity.com/paper/2402.18163/) (2024)

## Abstract (arXiv)

Deep learning-based face recognition models follow the common trend in deep neural networks by utilizing full-precision floating-point networks with high computational costs. Deploying such networks in use-cases constrained by computational requirements is often infeasible due to the large memory required by the full-precision model. Previous compact face recognition approaches proposed to design special compact architectures and train them from scratch using real training data, which may not be available in a real-world scenario due to privacy concerns. We present in this work the QuantFace solution based on low-bit precision format model quantization. QuantFace reduces the required computational cost of the existing face recognition models without the need for designing a particular architecture or accessing real training data. QuantFace introduces privacy-friendly synthetic face data to the quantization process to mitigate potential privacy concerns and issues related to the accessibility to real training data. Through extensive evaluation experiments on seven benchmarks and four network architectures, we demonstrate that QuantFace can successfully reduce the model size up to 5x while maintaining, to a large degree, the verification performance of the full-precision model without accessing real training datasets.