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Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild

Feng, Zhenhua, Huber, Patrik, Kittler, Josef, Hancock, P, Wu, X-J, Zhao, Q, Koppen, Paul and Ratsch, M (2018) Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild In: 13th IEEE International Conference on Automatic Face & Gesture Recognition Conference, 15-18 May 2018, Xi'an, China.

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Abstract

This paper investigates the evaluation of dense 3D face reconstruction from a single 2D image in the wild. To this end, we organise a competition that provides a new benchmark dataset that contains 2000 2D facial images of 135 subjects as well as their 3D ground truth face scans. In contrast to previous competitions or challenges, the aim of this new benchmark dataset is to evaluate the accuracy of a 3D dense face reconstruction algorithm using real, accurate and high-resolution 3D ground truth face scans. In addition to the dataset, we provide a standard protocol as well as a Python script for the evaluation. Last, we report the results obtained by three state-of-the-art 3D face reconstruction systems on the new benchmark dataset. The competition is organised along with the 2018 13th IEEE Conference on Automatic Face & Gesture Recognition.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Feng, Zhenhuaz.feng@surrey.ac.uk
Huber, Patrikph00111@surrey.ac.uk
Kittler, JosefJ.Kittler@surrey.ac.uk
Hancock, P
Wu, X-J
Zhao, Q
Koppen, Paulp.koppen@surrey.ac.uk
Ratsch, M
Date : 2018
Funders : EPSRC
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Melanie Hughes
Date Deposited : 27 Mar 2018 15:30
Last Modified : 27 Mar 2018 15:30
URI: http://epubs.surrey.ac.uk/id/eprint/846089

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