Graphic of the results

This is the graphic of the results obtained during the analysis of all the metodologies.
The graph shows the % of recognition vs # of faces that each system can recognize.

State of the art research results

These are my findings on the sate of the art, for face recognition.

Num Publication Name

1 Face Recognition Using the Weighted Fractal Neighbor Distance
2 Face Recognition Using Line Edge Map
3 Face Recognition Based on Fitting a 3D Morphable Model
4 GA-Fisher: A New LDA-Based Face Recognition Algorithm With Selection of Principal Components
5 Deformation Analysis for 3D Face Matching
6 Face Recognition Using Laplacianfaces
7 Face Detection and Identification Using a Hierarchical Feed-forward Recognition Architecture
8 Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
9 Generalized 2D Principal Component Analysis
10 Locally Linear Discriminant Analysis for Multimodally Distributed Classes for Face Recognition with a Single Model Image
11 Appearance-Based Face Recognition and Light-Fields
12 Appearance-Based Face Recognition and Light-Fields
13 Bayesian Shape Localization for Face Recognition Using Global and Local Textures
14 High-Speed Face Recognition Based on Discrete Cosine Transform and RBF Neural Networks
15 Kernel Machine-Based One-Parameter Regularized Fisher Discriminant Method for Face Recognition
16 Nonlinearity and Optimal Component Analysis
17 Face Recognition Using Fuzzy Integral and Wavelet Decomposition Method
18 Face Recognition Using the Discrete Cosine Transform
19 Acquiring Linear Subspaces for Face Recognition under Variable Lighting
20 Face Recognition System Using Local Autocorrelations and Multiscale Integration
21 Combined Subspace Method Using Global and Local Features for Face Recognition
22 Gabor Wavelet Associative Memory for Face Recognition
23 N-feature neural network human face recognition
24 PROBABILISTIC MATCHING FOR FACE RECOGNITION
25 Face Recognition by Applying Wavelet Subband Representation and Kernel Associative Memory
26 Real-time Embedded Face Recognition for Smart Home
27 A Unified Framework for Subspace Face Recognition
28 Discriminative Common Vectors for Face Recognition
29 Wavelet-based PCA for Human Face Recognition
30 Face Recognition Using Artificial Neural Network Group-Based Adaptive Tolerance (GAT) Trees
31 Face Recognition Using Kernel Direct Discriminant Analysis Algorithms


Well that’s all.
Now the next step is to make an analysis of this data and justify my own research project, also to see which way I’ll go.