Evaluation of expression recognition techniques

Ira Cohen, Nicu Sebe, Yafei Sun, Michael S. Lew, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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. In particular we use Naive-Bayes classifiers and to learn the dependencies among different facial motion features we use Tree-Augmented Naive Bayes (TAN) classifiers. We also investigate a neural network approach. Further, we propose an architecture of hidden Markov models (HMMs) for automatically segmenting and recognizing human facial expression from video sequences. We explore both person-dependent and person-independent recognition of expressions and compare the different methods.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsErwin M. Bakker, Michael S. Lew, Thomas S. Huang, Nicu Sebe, Xiang Zhou
PublisherSpringer-Verlag Berlin Heidelberg
Pages184-195
Number of pages12
ISBN (Print)9783540451136
DOIs
StatePublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2728
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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