Evaluation of expression recognition techniques

Authors:
Ira Cohen;Nicu Sebe;Yafei Sun;Michael S. Lew;Thomas S. Huang
Affiliations:
Beckman Institute, University of Illinois at Urbana-Champaign;Faculty of Science, University of Amsterdam, The Netherlands and Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands;Faculty of Science, University of Amsterdam, The Netherlands;Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands;Beckman Institute, University of Illinois at Urbana-Champaign
Venue:
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Year:
2003

Citing 9
Cited 2

Coding, Analysis, Interpretation, and Recognition of Facial Expressions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations
Classifying Facial Actions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Facial Expressions: The State of the Art

IEEE Transactions on Pattern Analysis and Machine Intelligence
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality

Data Mining and Knowledge Discovery
Connected Vibrations: A Modal Analysis Approach for Non-Rigid Motion Tracking

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A unified approach to coding and interpreting face images

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Facial expression recognition from video sequences: temporal and static modeling

Computer Vision and Image Understanding - Special issue on Face recognition
Human expression recognition from motion using a radial basis function network architecture

IEEE Transactions on Neural Networks

The state of the art in image and video retrieval

CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
3D human face description: landmarks measures and geometrical features

Image and Vision Computing

Quantified Score

Hi-index	0.00

Visualization

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.