Learning a sparse codebook of facial and body microexpressions for emotion recognition
Name
Davis_Learning a.pdf
Size
817.86 KB
Format
Adobe PDF
Checksum (MD5)
55a0e5a2bbb5347775d7b58c82c925e4
Author(s)
Song, Yale
Morency, Louis-Philippe
Davis, Randall
Date Issued
December 2013
Journal
Proceedings of the 15th ACM on International conference on multimodal interaction (ICMI '13)
Publisher
Association for Computing Machinery (ACM)
Citation
Yale Song, Louis-Philippe Morency, and Randall Davis. 2013. Learning a sparse codebook of facial and body microexpressions for emotion recognition. In Proceedings of the 15th ACM on International conference on multimodal interaction (ICMI '13). ACM, New York, NY, USA, 237-244.
Version
Author's final manuscript
Abstract
Obtaining a compact and discriminative representation of facial and body expressions is a difficult problem in emotion recognition. Part of the difficulty is capturing microexpressions, i.e., short, involuntary expressions that last for only a fraction of a second: at a micro-temporal scale, there are so many other subtle face and body movements that do not convey semantically meaningful information. We present a novel approach to this problem by exploiting the sparsity of the frequent micro-temporal motion patterns. Local space-time features are extracted over the face and body region for a very short time period, e.g., few milliseconds. A codebook of microexpressions is learned from the data and used to encode the features in a sparse manner. This allows us to obtain a representation that captures the most salient motion patterns of the face and body at a micro-temporal scale. Experiments performed on the AVEC 2012 dataset show our approach achieving the best published performance on the arousal dimension based solely on visual features. We also report experimental results on audio-visual emotion recognition, comparing early and late data fusion techniques.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2522848.2522851
