Parametric temporal alignment for the detection of facial action temporal segments
In Proceedings British Machine Vision Conference 2014
http://dx.doi.org/10.5244/C.28.102
Abstract
In this paper we propose a very first weakly supervised approach for detecting facial action unit temporal segments. This is achieved by means of behaviour similarity matching, where no training of dedicated classifier is needed and the input facial behaviour episode is compared to a template. The inferred temporal segment boundaries of the test sequence are those transferred from the template sequence. To this end, a parametric temporal alignment algorithm is proposed to align a single exemplar sequence to the test sequence. The proposed strategy can accommodate flexible time warp functions, does not need to exhaustively align all frames in both sequences, and the optimal warp parameters can be found by an efficient Gauss-Newton gradient descent search. We show that our approach produces the best results to date for the problem at hand, and provides the promising opportunity to studying facial actions from a new perspective.
Session
Poster Session
Files
Extended Abstract (PDF, 1 page, 154K)Paper (PDF, 11 pages, 447K)
Bibtex File
Citation
Bihan Jiang, Brais Martinez, and Maja Pantic. Parametric temporal alignment for the detection of facial action temporal segments. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.
BibTex
@inproceedings{BMVC.28.102
title = {Parametric temporal alignment for the detection of facial action temporal segments},
author = {Jiang, Bihan and Martinez, Brais and Pantic, Maja},
year = {2014},
booktitle = {Proceedings of the British Machine Vision Conference},
publisher = {BMVA Press},
editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
doi = { http://dx.doi.org/10.5244/C.28.102 }
}