Master Thesis - Department of Computer Science

Statistical
Linear Subspaces Nonlinear Subspaces Transformation based Others
i) Eigenfaces (PCA)
iii) Fisherfaces (LDA)
iv) Bayesian Methods (MAP and ML)
v) ICA and source separation
Template based Methods
Neural Network
i) Feature based
backpropagation NN
ii) Dynamic Link
Architecture (DLA)
iii) Single Layer Adaptive NN
iv) Multilayer Perceptron (MLP)
v) Probabilistic Decision Based
Neural Network (PDBNN)
vi) Self−organizing Map (SOM)
vii) Hopfield memory
ii) Probabilistic Eigenspaces (PPCA)
vi) Tensorfaces (Multi−Linear SVD)
vii) Two dimensional PCA (2D−PCA)
viii) Two dimensional LDA (2D−LDA)
ix) Discriminative Common Vectors (DCV)
Face Recognition Approaches
Hybrid Others
ii) Elastic Bunch Graph
Matching (EBGM)
i) PCA and RBF
i) Principal Curves and
Nonlinear PCA
ii) Kernel−PCA
iii) Kernel−LDA
i) Range Data
Geometry feature based Methods
ii) Infrared Scanning
iii) Profile Images
i) DCT
ii) DCT and HMM
iii) Local Feature
Analysis (LFA)
iii) Fourier Transform (FT)
Figure 2.1: Summary of approaches to face recognition.
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i) Active Shape Model
i) SVM