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