A novel real time system for facial expression recognition

Authors:
Xiaoyi Feng;Matti Pietikäinen;Abdenour Hadid;Hongmei Xie
Affiliations:
College of Electronics and Information, Northwestern Polytechnic University, Xi’an, China;Machine Vision Group, Infotech Oulu and Dept. of Electrical and Information Engineering, University of Oulu, Finland;Machine Vision Group, Infotech Oulu and Dept. of Electrical and Information Engineering, University of Oulu, Finland;College of Electronics and Information, Northwestern Polytechnic University, Xi’an, China
Venue:
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Year:
2005

Citing 5
Cited 1

Automatic Analysis of Facial Expressions: The State of the Art

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Real time facial expression recognition in video using support vector machines

Proceedings of the 5th international conference on Multimodal interfaces
Real-Time Facial Expression Recognition Based on Boosted Embedded Hidden Markov Model

ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Real-time emotion recognition using biologically inspired models

AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication

LBP-guided active contours

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a fully automatic, real-time system is proposed to recognize seven basic facial expressions (angry, disgust, fear, happiness, neutral, sadness and surprise), which is insensitive to illumination changes. First, face is located and normalized based on an illumination insensitive skin model and face segmentation; then, the basic Local Binary Patterns (LBP) technique, which is invariant to monotonic grey level changes, is used for facial feature extraction; finally, a coarse-to-fine scheme is used for expression classification. Theoretical analysis and experimental results show that the proposed system performs well in variable illumination and some degree of head rotation.