A simple approach to facial expression recognition

Authors:
Mu-Chun Su;Yi-Jwu Hsieh;De-Yuan Huang
Affiliations:
Department of Computer Science & Information Engineering, National Central University, Taiwan, R.O.C.;Department of Computer Science & Information Engineering, National Central University, Taiwan, R.O.C.;Department of Computer Science & Information Engineering, National Central University, Taiwan, R.O.C.
Venue:
CEA'07 Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and Applications
Year:
2007

Citing 19
Cited 1

An Investigation of Smoothness Constraints for the Estimation of Displacement Vector Fields from Image Sequences

IEEE Transactions on Pattern Analysis and Machine Intelligence
EMPATH: face, emotion, and gender recognition using holons

NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Automatic recognition and analysis of human faces and facial expressions: a survey

Pattern Recognition
Feature extraction from faces using deformable templates

International Journal of Computer Vision
Recognition of human facial expressions without feature extraction

ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Recognizing Human Facial Expressions From Long Image Sequences Using Optical Flow

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion

International Journal of Computer Vision
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
Recognizing Action Units for Facial Expression Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis and Synthesis of Facial Image Sequences Using Physical and Anatomical Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-Point Tracking by Optical Flow Discriminates Subtle Differences in Facial Expression

FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Spotting Segments Displaying Facial Expression from Image Sequences Using HMM

FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comprehensive Database for Facial Expression Analysis

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Evaluation of Gabor-Wavelet-Based Facial Action Unit Recognition in Image Sequences of Increasing Complexity

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Human expression recognition from motion using a radial basis function network architecture

IEEE Transactions on Neural Networks
Face recognition/detection by probabilistic decision-based neural network

IEEE Transactions on Neural Networks

Emotion-oriented eCommerce systems

WSEAS TRANSACTIONS on SYSTEMS

Quantified Score

Hi-index	0.00

Visualization

Abstract

Human face-to-face communication plays an important role in human communication and interaction. In recent years, several different approaches have been proposed for developing methods of automatic facial expression analysis. In this paper, a simple approach to automatic facial expression recognition is presented. The proposed system is able to automatically perform human face detection, feature point extraction and facial expression recognition from image sequences. The facial recognition procedures involves in two stages. At the first stage, three multi-layer perceptrons (MLPs) are separately trained to recognize action units involving in the eyebrows, the eyes and the mouth regions. Then individual expression network was trained to recognize five basic facial expressions based on the outputs computed from the aforementioned three MLPs. Experiments were conducted to test the performance of the proposed facial expression recognition system.