Patch-wise low-dimensional probabilistic linear discriminant analysis for Face Recognition.
Vitomir StrucNikola PavesicJerneja Zganec-GrosBostjan VesnicerPublished in: ICASSP (2013)
Keyphrases
- linear discriminant analysis
- low dimensional
- dimensionality reduction
- face recognition
- principal component analysis
- high dimensional data
- discriminant analysis
- dimension reduction
- high dimensional
- feature space
- discriminant features
- small sample size
- feature extraction
- subspace methods
- manifold learning
- null space
- high dimensionality
- subspace analysis
- data points
- pattern recognition
- fisher criterion
- linear subspace
- subspace learning
- face images
- euclidean space
- input space
- lower dimensional
- discriminative information
- sparse representation
- linear discriminant
- principal components
- image patches
- generative model
- support vector
- dimensionality reduction methods
- human faces
- recognition rate
- kernel discriminant analysis
- data mining
- scatter matrix
- generalized discriminant analysis
- graph embedding
- singular value decomposition
- face detection
- support vector machine svm
- pairwise
- locally linear embedding
- facial expression recognition
- face databases
- scatter matrices
- feature selection
- neural network
- nonparametric discriminant analysis