# Do We Really Need to Collect Millions of Faces for Effective Face Recognition?

arXiv: https://arxiv.org/abs/1603.07057 · HTML: https://mlsanity.com/paper/1603.07057/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2016.

## Built on by (most cited first, 17 of 17)

- [Protecting Celebrities from DeepFake with Identity Consistency Transformer](https://mlsanity.com/paper/2203.01318/) (2022)
- [LARNet: Lie Algebra Residual Network for Face Recognition](https://mlsanity.com/paper/2103.08147/) (2021)
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- [Beyond Real Faces: Synthetic Datasets Can Achieve Reliable Recognition Performance without Privacy Compromise](https://mlsanity.com/paper/2510.17372/) (2025)
- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)
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- [Learning Domain and Pose Invariance for Thermal-to-Visible Face Recognition](https://mlsanity.com/paper/2211.09350/) (2022)
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- [GCCN: Global Context Convolutional Network](https://mlsanity.com/paper/2110.11664/) (2021)
- [Analyzing the Impact of Shape & Context on the Face Recognition Performance of Deep Networks](https://mlsanity.com/paper/2208.02991/) (2022)
- [2D-3D Attention and Entropy for Pose Robust 2D Facial Recognition](https://mlsanity.com/paper/2505.09073/) (2025)
- [Unsupervised Modular Adaptive Region Growing and RegionMix Classification for Wind Turbine Segmentation](https://mlsanity.com/paper/2601.04065/) (2026)
- [IntraLoss: Further Margin via Gradient-Enhancing Term for Deep Face Recognition](https://mlsanity.com/paper/2107.03352/) (2021)
- [A Survey on Face Recognition Systems](https://mlsanity.com/paper/2201.02991/) (2022)
- [Age Prediction From Face Images Via Contrastive Learning](https://mlsanity.com/paper/2308.11896/) (2023)
- [3D2M Dataset: A 3-Dimension diverse Mesh Dataset](https://mlsanity.com/paper/2410.07415/) (2024)

## Abstract (arXiv)

Face recognition capabilities have recently made extraordinary leaps. Though this progress is at least partially due to ballooning training set sizes -- huge numbers of face images downloaded and labeled for identity -- it is not clear if the formidable task of collecting so many images is truly necessary. We propose a far more accessible means of increasing training data sizes for face recognition systems. Rather than manually harvesting and labeling more faces, we simply synthesize them. We describe novel methods of enriching an existing dataset with important facial appearance variations by manipulating the faces it contains. We further apply this synthesis approach when matching query images represented using a standard convolutional neural network. The effect of training and testing with synthesized images is extensively tested on the LFW and IJB-A (verification and identification) benchmarks and Janus CS2. The performances obtained by our approach match state of the art results reported by systems trained on millions of downloaded images.