Facial expression recognition using fisher weight maps

Authors:
Yusuke Shinohara;Nobuyuki Otsu
Affiliations:
Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan;Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan and National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan
Venue:
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Year:
2004

Citing 8
Cited 14

Face Recognition System Using Local Autocorrelations and Multiscale Integration

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching

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
Automatic Classification of Single Facial Images

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
Coding Facial Expressions with Gabor Wavelets

FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Smile Detection for User Interfaces

ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Integrating a mixed-feature model and multiclass support vector machine for facial expression recognition

Integrated Computer-Aided Engineering
Facial expression recognition system based on rigid and non-rigid motion separation and 3D pose estimation

Pattern Recognition
Feature-adaptive motion energy analysis for facial expression recognition

ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Soccer formation classification based on fisher weight map and Gaussian mixture models

LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
Expression recognition method of image sequence in audio-video

IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Improving local descriptors by embedding global and local spatial information

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Boosted independent features for face expression recognition

ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Automatic detection of facial feature points via HOGs and geometric prior models

IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Facial expression recognition using radial encoding of local Gabor features and classifier synthesis

Pattern Recognition
Automatic facial expression recognition with AAM-Based feature extraction and SVM classifier

MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Human facial expression recognition using hybrid network of PCA and RBFN

ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Expression recognition using elastic graph matching

ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
BROAFERENCE: a next generation multimedia terminal providing direct feedback on audience’s satisfaction level

INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction

Quantified Score

Hi-index	0.00

Visualization

Abstract

In recent years, much research has been done on face image analysis. There are two major approaches: local-feature-based and image-vector-based. We propose a hybrid of these two approaches. Our method uses Higherorder Local Auto-Correlation (HLAC) features and Fisher weight maps. HLAC features are computed at each pixel in an image. These features are integrated with a weight map to obtain a feature vector. The optimal weight map, called a Fisher weight map, is found by maximizing the Fisher criterion of feature vectors. Fisher discriminant analysis is used to recognize an image from the feature vector. Our experiments on facial expression recognition demonstrate the effectiveness of Fisher weight maps for objectively quantifying the importance of each facial area for classification of expressions.