Publication

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Conference on Computer Vision and Pattern Recognition (CVPR)


Abstract

In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4,000 identities.

The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.

Related Publications

All Publications

MICCAI - October 5, 2020

Active MR k-space Sampling with Reinforcement Learning

Luis Pineda, Sumana Basu, Adriana Romero, Roberto Calandra, Michal Drozdzal

Multimodal Video Analysis Workshop at ECCV - August 23, 2020

Audio-Visual Instance Discrimination

Pedro Morgado, Nuno Vasconcelos, Ishan Misra

ICML - November 3, 2020

Learning Near Optimal Policies with Low Inherent Bellman Error

Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill

AISTATS - November 3, 2020

A single algorithm for both restless and rested rotting bandits

Julien Seznec, Pierre Menard, Alessandro Lazaric, Michal Valko

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