# Web-Scale Training for Face Identification

arXiv: https://arxiv.org/abs/1406.5266 · HTML: https://mlsanity.com/paper/1406.5266/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2014.

## Built on by (most cited first, 20 of 20)

- [WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2103.04098/) (2021)
- [DigiFace-1M: 1 Million Digital Face Images for Face Recognition](https://mlsanity.com/paper/2210.02579/) (2022)
- [Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection](https://mlsanity.com/paper/2203.02194/) (2022)
- [WebFace260M: A Benchmark for Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2204.10149/) (2022)
- [Synthetic Data for Face Recognition: Current State and Future Prospects](https://mlsanity.com/paper/2305.01021/) (2023)
- [Masked Face Recognition Challenge: The WebFace260M Track Report](https://mlsanity.com/paper/2108.07189/) (2021)
- [ONOT: a High-Quality ICAO-compliant Synthetic Mugshot Dataset](https://mlsanity.com/paper/2404.11236/) (2024)
- [Beyond Real Faces: Synthetic Datasets Can Achieve Reliable Recognition Performance without Privacy Compromise](https://mlsanity.com/paper/2510.17372/) (2025)
- [FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders](https://mlsanity.com/paper/2205.11090/) (2022)
- ["Train one, Classify one, Teach one" -- Cross-surgery transfer learning for surgical step recognition](https://mlsanity.com/paper/2102.12308/) (2021)
- [ADD: An Automatic Desensitization Fisheye Dataset for Autonomous Driving](https://mlsanity.com/paper/2308.07590/) (2023)
- [Face-GCN: A Graph Convolutional Network for 3D Dynamic Face Identification/Recognition](https://mlsanity.com/paper/2104.09145/) (2021)
- [Open-set Face Recognition for Small Galleries Using Siamese Networks](https://mlsanity.com/paper/2105.06967/) (2021)
- [Autoencoder Based Face Verification System](https://mlsanity.com/paper/2312.14301/) (2023)
- [3D Face Recognition: A Survey](https://mlsanity.com/paper/2108.11082/) (2021)
- [Efficient Verification-Based Face Identification](https://mlsanity.com/paper/2312.13240/) (2023)
- [Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layer](https://mlsanity.com/paper/2405.01662/) (2024)
- [Celeb-FBI: A Benchmark Dataset on Human Full Body Images and Age, Gender, Height and Weight Estimation using Deep Learning Approach](https://mlsanity.com/paper/2407.03486/) (2024)
- [Mixing between the Cross Entropy and the Expectation Loss Terms](https://mlsanity.com/paper/2109.05635/) (2021)
- [DiffProb: Data Pruning for Face Recognition](https://mlsanity.com/paper/2505.15272/) (2025)

## Abstract (arXiv)

Scaling machine learning methods to very large datasets has attracted considerable attention in recent years, thanks to easy access to ubiquitous sensing and data from the web. We study face recognition and show that three distinct properties have surprising effects on the transferability of deep convolutional networks (CNN): (1) The bottleneck of the network serves as an important transfer learning regularizer, and (2) in contrast to the common wisdom, performance saturation may exist in CNN's (as the number of training samples grows); we propose a solution for alleviating this by replacing the naive random subsampling of the training set with a bootstrapping process. Moreover, (3) we find a link between the representation norm and the ability to discriminate in a target domain, which sheds lights on how such networks represent faces. Based on these discoveries, we are able to improve face recognition accuracy on the widely used LFW benchmark, both in the verification (1:1) and identification (1:N) protocols, and directly compare, for the first time, with the state of the art Commercially-Off-The-Shelf system and show a sizable leap in performance.