Multi-velocity neural networks for facial expression recognition in videos
Name
1603.06829.pdf
Description
Submitted version
Size
1.98 MB
Format
Adobe PDF
Checksum (MD5)
d5539743fb3457ffedff31aba25efdc1
Author(s)
Gupta, Otkrist
Raviv, Dan
Raskar, Ramesh
Date Issued
2019
Journal
IEEE Transactions on Affective Computing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Original manuscript
Abstract
© 2010-2012 IEEE. We present a new action recognition deep neural network which adaptively learns the best action velocities in addition to the classification. While deep neural networks have reached maturity for image understanding tasks, we are still exploring network topologies and features to handle the richer environment of video clips. Here, we tackle the problem of multiple velocities in action recognition, and provide state-of-the-art results for facial expression recognition, on known and new collected datasets. We further provide the training steps for our semi-supervised network, suited to learn from huge unlabeled datasets with only a fraction of labeled examples.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TAFFC.2017.2713355
