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

ICML - July 24, 2021

Using Bifurcations for Diversity in Differentiable Games

Jonathan Lorraine, Jack Parker-Holder, Paul Vicol, Aldo Pacchiano, Luke Metz, Tal Kachman, Jakob Foerster

arXiv - July 8, 2021

First-Generation Inference Accelerator Deployment at Facebook

Michael Anderson, Benny Chen, Stephen Chen, Summer Deng, Jordan Fix, Michael Gschwind, Aravind Kalaiah, Changkyu Kim, Jaewon Lee, Jason Liang, Haixin Liu, Yinghai Lu, Jack Montgomery, Arun Moorthy, Satish Nadathur, Sam Naghshineh, Avinash Nayak, Jongsoo Park, Chris Petersen, Martin Schatz, Narayanan Sundaram, Bangsheng Tang, Peter Tang, Amy Yang, Jiecao Yu, Hector Yuen, Ying Zhang, Aravind Anbudurai, Vandana Balan, Harsha Bojja, Joe Boyd, Matthew Breitbach, Claudio Caldato, Anna Calvo, Garret Catron, Sneh Chandwani, Panos Christeas, Brad Cottel, Brian Coutinho, Arun Dalli, Abhishek Dhanotia, Oniel Duncan, Roman Dzhabarov, Simon Elmir, Chunli Fu, Wenyin Fu, Michael Fulthorp, Adi Gangidi, Nick Gibson, Sean Gordon, Beatriz Padilla Hernandez, Daniel Ho, Yu-Cheng Huang, Olof Johansson, Shishir Juluri, Shobhit Kanaujia, Manali Kesarkar, Jonathan Killinger, Ben Kim, Rohan Kulkarni, Meghan Lele, Huayu Li, Huamin Li, Yueming Li, Cynthia Liu, Jerry Liu, Bert Maher, Chandra Mallipedi, Seema Mangla, Kiran Kumar Matam, Jubin Mehta, Shobhit Mehta, Christopher Mitchell, Bharath Muthiah, Nitin Nagarkatte, Ashwin Narasimha, Bernard Nguyen, Thiara Ortiz, Soumya Padmanabha, Deng Pan, Ashwin Poojary, Ye (Charlotte) Qi, Olivier Raginel, Dwarak Rajagopal, Tristan Rice, Craig Ross, Nadav Rotem, Scott Russ, Kushal Shah, Baohua Shan, Hao Shen, Pavan Shetty, Krish Skandakumaran, Kutta Srinivasan, Roshan Sumbaly, Michael Tauberg, Mor Tzur, Hao Wang, Man Wang, Ben Wei, Alex Xiao, Chenyu Xu, Martin Yang, Kai Zhang, Ruoxi Zhang, Ming Zhao, Whitney Zhao, Rui Zhu, Lin Qiao, Misha Smelyanskiy, Bill Jia, Vijay Rao

UAI - July 23, 2021

High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces

David Eriksson, Martin Jankowiak

ICML - July 18, 2021

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, St├ęphane Deny

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