Evaluation of expression recognition techniques

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

Research output: Contribution to journalReview article

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)
Pages (from-to)184-195
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2728
StatePublished - Dec 1 2003

Fingerprint

Facial Expression
Naive Bayes Classifier
Classifiers
Person
Evaluation
Bayesian networks
Hidden Markov models
Bayesian Networks
Markov Model
Classifier
Neural Networks
Neural networks
Motion
Dependent
Human

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Evaluation of expression recognition techniques. / Cohen, Ira; Sebe, Nicu; Sun, Yafei; Lew, Michael S.; Huang, Thomas S.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 2728, 01.12.2003, p. 184-195.

Research output: Contribution to journalReview article

@article{21213867e4964b55be716b4401d59193,
title = "Evaluation of expression recognition techniques",
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.",
author = "Ira Cohen and Nicu Sebe and Yafei Sun and Lew, {Michael S.} and Huang, {Thomas S.}",
year = "2003",
month = "12",
day = "1",
language = "English (US)",
volume = "2728",
pages = "184--195",
journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
issn = "0302-9743",
publisher = "Springer Verlag",

}

TY - JOUR

T1 - Evaluation of expression recognition techniques

AU - Cohen, Ira

AU - Sebe, Nicu

AU - Sun, Yafei

AU - Lew, Michael S.

AU - Huang, Thomas S.

PY - 2003/12/1

Y1 - 2003/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=35248831738&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=35248831738&partnerID=8YFLogxK

M3 - Review article

AN - SCOPUS:35248831738

VL - 2728

SP - 184

EP - 195

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SN - 0302-9743

ER -