Facial expression recognition from video sequences: Temporal and static modeling

Ira Cohen, Nicu Sebe, Ashutosh Garg, Lawrence S. Chen, Thomas S. Huang

Research output: Contribution to journalArticlepeer-review


The most expressive way humans display emotions is through facial expressions. In this work we report on several advances we have made in building a system for classification of facial expressions from continuous video input. We introduce and test different Bayesian network classifiers for classifying expressions from video, focusing on changes in distribution assumptions, and feature dependency structures. In particular we use Naive-Bayes classifiers and change the distribution from Gaussian to Cauchy, and use Gaussian Tree-Augmented Naive Bayes (TAN) classifiers to learn the dependencies among different facial motion features. We also introduce a facial expression recognition from live video input using temporal cues. We exploit the existing methods and propose a new architecture of hidden Markov models (HMMs) for automatically segmenting and recognizing human facial expression from video sequences. The architecture performs both segmentation and recognition of the facial expressions automatically using a multi-level architecture composed of an HMM layer and a Markov model layer. We explore both person-dependent and person-independent recognition of expressions and compare the different methods.

Original languageEnglish (US)
Pages (from-to)160-187
Number of pages28
JournalComputer Vision and Image Understanding
Issue number1-2
StatePublished - 2003

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Facial expression recognition from video sequences: Temporal and static modeling'. Together they form a unique fingerprint.

Cite this