Learning a sparse codebook of facial and body microexpressions for emotion recognition
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 a...
Main Authors: | Song, Yale, Morency, Louis-Philippe, Davis, Randall |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
Format: | Article |
Language: | en_US |
Published: |
Association for Computing Machinery (ACM)
2014
|
Online Access: | http://hdl.handle.net/1721.1/86124 https://orcid.org/0000-0001-5232-7281 |
Similar Items
-
Action Recognition by Hierarchical Sequence Summarization
by: Song, Yale, et al.
Published: (2014) -
Multi-view latent variable discriminative models for action recognition
by: Song, Yale, et al.
Published: (2014) -
Distribution-sensitive learning for imbalanced datasets
by: Song, Yale, et al.
Published: (2014) -
Multimodal human behavior analysis: Learning correlation and interaction across modalities
by: Song, Yale, et al.
Published: (2014) -
Robust representation and recognition of facial emotions using extreme sparse learning
by: Li, Jun, et al.
Published: (2015)