Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Image Analysis by Unsupervised Learning
Face Image Analysis by Unsupervised Learning
PCA = Gabor for Expression Recognition
PCA = Gabor for Expression Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Dynamics of Facial Expression Extracted Automatically from Video
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Probabilistic expression analysis on manifolds
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
This paper is engaged in the holistic spatial analysis on facial expression images. We present a systematic comparison of machine learning methods applied to the problem of automatic facial expression recognition, including supervised and unsupervised subspace analysis, SVM classifier and their nonlinear versions. Image-based holistic spatial analysis is more adaptive to recognition task in that it automatically learns the inner structure of training samples and extracts the most pertinent features for classification. Nonlinear analysis methods which could extract higher order dependencies among input patterns are supposed to promote the performance of classification. Surprisingly, the linear classifiers outperformed their nonlinear versions in our experiments. We proposed a new feature selection method named the Weighted Saliency Maps(WSM). Compared to other feature selection schemes such as Adaboost and PCA, WSM has the advantage of being simple, fast and flexible.