The nature of statistical learning theory
The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Classification of Single Facial Images
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
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Robust Real-Time Face Detection
International Journal of Computer Vision
Evaluation of Face Resolution for Expression Analysis
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
The CSU face identification evaluation system: its purpose, features, and structure
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Automatic facial expression recognition is an interesting and challenging subject in signal processing, pattern recognition, artificial intelligence, etc. In this paper, a new method of facial expression recognition based on local binary patterns (LBP) and local Fisher discriminant analysis (LFDA) is presented. The LBP features are firstly extracted from the original facial expression images. Then LFDA is used to produce the low dimensional discriminative embedded data representations from the extracted high dimensional LBP features with striking performance improvement on facial expression recognition tasks. Finally, support vector machines (SVM) classifier is used for facial expression classification. The experimental results on the popular JAFFE facial expression database demonstrate that the presented facial expression recognition method based on LBP and LFDA obtains the best recognition accuracy of 90.7% with 11 reduced features, outperforming the other used methods such as principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP).