A discriminative parts based model approach for fiducial points free and shape constrained head pose normalisation in the wild

Abhinav Dhall, Karan Sikka, Gwen Littlewort, Roland Goecke, Marian Bartlett

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

5 Citations (Scopus)

Abstract

This paper proposes a method for parts-based view-invariant head pose normalisation, which works well even in difficult real-world conditions. Handling pose is a classical problem in facial analysis. Recently, parts-based models have shown promising performance for facial landmark points detection 'in the wild'. Leveraging on the success of these models, the proposed data-driven regression framework computes a constrained normalised virtual frontal head pose. The response maps of a discriminatively trained part detector are used as texture information. These sparse texture maps are projected from non-frontal to frontal pose using block-wise structured regression. Finally, a facial kinematic shape constraint is achieved by applying a shape model. The advantages of the proposed approach are: a) no explicit dependence on the outputs of a facial parts detector and, thus, avoiding any error propagation owing to their failure; (b) the application of a shape prior on the reconstructed frontal maps provides an anatomically constrained facial shape; and c) modelling head pose as a mixture-of-parts model allows the framework to work without any prior pose information. Experiments are performed on the Multi-PIE and the 'in the wild' SFEW databases. The results demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
EditorsWalter Scheirer, Ruigang Yang, Charles Stewart
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1028-1035
Number of pages8
ISBN (Print)9781479949854
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventIEEE Winter Conference on Applications of Computer Vision 2014 - Steamboat Springs, United States of America
Duration: 24 Mar 201426 Mar 2014
http://www.wacv14.org/

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision 2014
Abbreviated titleWACV 2014
CountryUnited States of America
CitySteamboat Springs
Period24/03/1426/03/14
Internet address

Cite this

Dhall, A., Sikka, K., Littlewort, G., Goecke, R., & Bartlett, M. (2014). A discriminative parts based model approach for fiducial points free and shape constrained head pose normalisation in the wild. In W. Scheirer, R. Yang, & C. Stewart (Eds.), 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 (pp. 1028-1035). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/WACV.2014.6835991