@inproceedings{b31be049404942df9546d3147cfd55ba,
title = "Subtle expression recognition using optical strain weighted features",
abstract = "Optical strain characterizes the relative amount of displacement by a moving object within a time interval. Its ability to compute any small muscular movements on faces can be advantageous to subtle expression research. This paper proposes a novel optical strain weighted feature extraction scheme for subtle facial micro-expression recognition. Motion information is derived from optical strain magnitudes, which is then pooled spatio-temporally to obtain block-wise weights for the spatial image plane. By simple product with the weights, the resulting feature histograms are intuitively scaled to accommodate the importance of block regions. Experiments conducted on two recent spontaneous micro-expression databases–CASMEII and SMIC, demonstrated promising improvement over the baseline results.",
author = "Liong, {Sze Teng} and John See and Phan, {Raphael C.W.} and {Le Ngo}, {Anh Cat} and Oh, {Yee Hui} and Wong, {Kok Sheik}",
note = "Funding Information: Work done in project UbeAware funded by TM. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015. Copyright: Copyright 2015 Elsevier B.V., All rights reserved.; Workshop on Computer Vision for Affective Computing 2014, CV4AC 2014 ; Conference date: 01-11-2014 Through 02-11-2014",
year = "2015",
doi = "10.1007/978-3-319-16631-5_47",
language = "English",
isbn = "9783319166308",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "644--657",
editor = "C.V. Jawahar and Shiguang Shan",
booktitle = "Computer Vision - ACCV 2014 Workshops, Revised Selected Papers",
}