Multi-velocity neural networks for facial expression recognition in videos

Otkrist Gupta, Dan Raviv, Ramesh Raskar

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number7942120
Pages (from-to)290-296
Number of pages7
JournalIEEE Transactions on Affective Computing
Volume10
Issue number2
DOIs
StatePublished - 1 Apr 2019

Keywords

  • Deep learning
  • gesture recognition
  • machine learning
  • neural nets
  • video classification

Fingerprint

Dive into the research topics of 'Multi-velocity neural networks for facial expression recognition in videos'. Together they form a unique fingerprint.

Cite this