University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Cascaded collaborative regression for robust facial landmark detection trained using a mixture of synthetic and real images with dynamic weighting

Feng, Z-H, Hu, G, Kittler, J, Christmas, WJ and Wu, X-J (2015) Cascaded collaborative regression for robust facial landmark detection trained using a mixture of synthetic and real images with dynamic weighting IEEE Transactions on Image Processing, 24 (11). pp. 3425-3440.

[img]
Preview
Text
Feng-TIP-2015.pdf - ["content_typename_Accepted version (post-print)" not defined]
Available under License : See the attached licence file.

Download (5MB) | Preview
[img]
Preview
PDF (licence)
SRI_deposit_agreement.pdf
Available under License : See the attached licence file.

Download (33kB) | Preview

Abstract

A large amount of training data is usually crucial for successful supervised learning. However, the task of providing training samples is often time-consuming, involving a considerable amount of tedious manual work. Also the amount of training data available is often limited. As an alternative, in this paper, we discuss how best to augment the available data for the application of automatic facial landmark detection (FLD). We propose the use of a 3D morphable face model to generate synthesised faces for a regression-based detector training. Benefiting from the large synthetic training data, the learned detector is shown to exhibit a better capability to detect the landmarks of a face with pose variations. Furthermore, the synthesised training dataset provides accurate and consistent landmarks as compared to using manual landmarks, especially for occluded facial parts. The synthetic data and real data are from different domains; hence the detector trained using only synthesised faces does not generalise well to real faces. To deal with this problem, we propose a cascaded collaborative regression (CCR) algorithm, which generates a cascaded shape updater that has the ability to overcome the difficulties caused by pose variations, as well as achieving better accuracy when applied to real faces. The training is based on a mix of synthetic and real image data with the mixing controlled by a dynamic mixture weighting schedule. Initially the training uses heavily the synthetic data, as this can model the gross variations between the various poses. As the training proceeds, progressively more of the natural images are incorporated, as these can model finer detail. To improve the performance of the proposed algorithm further, we designed a dynamic multi-scale local feature extraction method, which captures more informative local features for detector training. An extensive evaluation on both controlled and uncontrolled face datasets demonstrates the merit of the proposed algorithm.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Feng, Z-HUNSPECIFIEDUNSPECIFIED
Hu, GUNSPECIFIEDUNSPECIFIED
Kittler, JUNSPECIFIEDUNSPECIFIED
Christmas, WJUNSPECIFIEDUNSPECIFIED
Wu, X-JUNSPECIFIEDUNSPECIFIED
Date : November 2015
Identification Number : 10.1109/TIP.2015.2446944
Additional Information : (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works
Depositing User : Symplectic Elements
Date Deposited : 19 Aug 2015 08:19
Last Modified : 19 Aug 2015 08:19
URI: http://epubs.surrey.ac.uk/id/eprint/808177

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800