Research Update

These past few days I’ve been writing down a table with some interesting information of the current state of the art, this information is: Name of the publication (transaction papers, journal papers, etc), PIE robustness, % of recognition, type of clasiffier, facial features extracted, year, database tested, and application. The papers done until now are this ones:

Deformation Analysis for 3D Face Matching Discriminative Common Vectors for Face Recognition
Face Recognition Using Laplacianfaces
High-Speed Face Recognition Based on Discrete Cosine Transform and RBF Neural Networks
Locally Linear Discriminant Analysis for Multimodally Distributed Classes for Face recognition with a Single Model Image
Wavelet-based PCA for Human Face Recognition
Real-time Embedded Face Recognition for Smart Home
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
Appearance-Based Face Recognition and Light-Fields Bayesian Shape Localization for Face Recognition Using Global and Local Textures
A Unified Framework for Subspace Face Recognition
PROBABILISTIC MATCHING FOR FACE RECOGNITION
Face Recognition Based on Fitting a 3D Morphable Model
Appearance-Based Face Recognition and Light-Fields
Face Recognition Using Artificial Neural Network Group-Based Adaptive Tolerance (GAT) Trees
Face Recognition by Applying Wavelet Subband Representation and Kernel Associative Memory
Face Recognition Using Kernel Direct Discriminant Analysis Algorithms
Face Recognition Using Fuzzy Integral and Wavelet Decomposition Method
Face Recognition Using Line Edge Map
Face Recognition Using the Discrete Cosine Transform
Face Recognition System Using Local Autocorrelations and Multiscale Integration
Face Recognition Using the Weighted Fractal Neighbor Distance
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition Gabor Wavelet Associative Memory for Face Recognition
N-feature neural network human face recognition
GA-Fisher: A New LDA-Based Face Recognition Algorithm With Selection of Principal Components
Kernel Machine-Based One-Parameter Regularized Fisher Discriminant Method for Face Recognition

Some authors try to get the reader confused about the results, trying to make himself look the best methodology with the best recognition rate. Here is where I loose the big part of the time, discovering the real % of recognition.