Web-Scale Training for Face Identification

The IEEE Conference on Computer Vision and Pattern Recognition


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.

Related Publications

All Publications

NeurIPS - December 6, 2020

High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization

Qing Feng, Benjamin Letham, Hongzi Mao, Eytan Bakshy

Innovative Technology at the Interface of Finance and Operations - March 31, 2021

Market Equilibrium Models in Large-Scale Internet Markets

Christian Kroer, Nicolas E. Stier-Moses

Human Interpretability Workshop at ICML - July 17, 2020

Investigating Effects of Saturation in Integrated Gradients

Vivek Miglani, Bilal Alsallakh, Narine Kokhlikyan, Orion Reblitz-Richardson

ICASSP - June 6, 2021

Multi-Channel Speech Enhancement Using Graph Neural Networks

Panagiotis Tzirakis, Anurag Kumar, Jacob Donley

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy