Recognizing Facial Expressions Using Subspace Linear Discriminant Analysis.
Siamak TafavoghÖnsen ToygarPublished in: IPCV (2009)
Keyphrases
- linear discriminant analysis
- recognizing facial expressions
- principal component analysis
- dimensionality reduction
- subspace methods
- null space
- high dimensional data
- linear projection
- face recognition
- facial expression recognition
- qr decomposition
- principal components analysis
- discriminant analysis
- feature space
- facial expressions
- feature extraction
- low dimensional
- locality preserving projections
- subspace analysis
- scatter matrices
- scatter matrix
- dimension reduction
- small sample size
- expression recognition
- lower dimensional
- fisher criterion
- discriminant information
- discriminative information
- subspace learning
- support vector
- facial features
- face images
- kernel pca
- methods such as principal component analysis
- linear subspace
- support vector machine svm
- subspace clustering
- principal components
- independent component analysis
- covariance matrix
- pattern recognition
- nearest neighbor
- data points
- decomposition method
- human faces
- high dimensional
- data sets
- neural network
- feature selection
- high resolution
- distance measure
- optimization criterion
- singular value decomposition
- face databases