# MegaFace: A Million Faces for Recognition at Scale

arXiv: https://arxiv.org/abs/1505.02108 · HTML: https://mlsanity.com/paper/1505.02108/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2015.

## Built on by (most cited first, 5 of 5)

- [SphereFace2: Binary Classification is All You Need for Deep Face Recognition](https://mlsanity.com/paper/2108.01513/) (2021)
- [SphereFace Revived: Unifying Hyperspherical Face Recognition](https://mlsanity.com/paper/2109.05565/) (2021)
- [Towards Explaining Demographic Bias through the Eyes of Face Recognition Models](https://mlsanity.com/paper/2208.13400/) (2022)
- [Revisiting Face Forgery Detection: From Facial Representation to Forgery Detection](https://mlsanity.com/paper/2409.16945/) (2024)
- [Towards classification-based representation learning for place recognition on LiDAR scans](https://mlsanity.com/paper/2511.00738/) (2025)

## Abstract (arXiv)

Recent face recognition experiments on the LFW benchmark show that face recognition is performing stunningly well, surpassing human recognition rates. In this paper, we study face recognition at scale. Specifically, we have collected from Flickr a Million faces and evaluated state of the art face recognition algorithms on this dataset. We found that the performance of algorithms varies--while all perform great on LFW, once evaluated at scale recognition rates drop drastically for most algorithms. Interestingly, deep learning based approach by citeschroff2015facenet performs much better, but still gets less robust at scale. We consider both verification and identification problems, and evaluate how pose affects recognition at scale. Moreover, we ran an extensive human study on Mechanical Turk to evaluate human recognition at scale, and report results. All the photos are creative commons photos and is released at smallurlhttp://megaface.cs.washington.edu/ for research and further experiments.