Face Recognition Technology


Face recognition

Face Recognition Based on PCA

DCT-ANN Face Identification

Wavelet-ANN Face Recognition

Face Recognition Based on Polar Frequency Features

Face Recognition Based on FisherFaces

Face Recognition Based on Local Features

Face Recognition in Fourier Space

WebCam Face Identification

Face Recognition Based on Overlapping DCT

Face Recognition Based on Statistical Moments

Face Recognition Based on Nonlinear PCA

Face Recognition Based on Hierarchical Dimensionality Reduction

Fusion of Low-Computational Global and Local Features For Face Recognition

SVD-Based Face Recognition

Correlation Filters Face Verification

ICA Face Recognition

3D Face Recognition

Infrared Face Recognition

Octave Face Recognition

PHP Face Recognition

JAVA Face Recognition

LBP Face Recognition System

HMM Face Recognition System

NMF Face Recognition System

Face matching

Face Identification Based on CPD

GA MACE Face Verification

External resources

Advanced Source Code .Com

Neural Networks .It

Genetic Algorithms .It

Iris Recognition .It

Face Recognition Using Low-Computational Features

Download now Matlab source code
Requirements: Matlab, Matlab Image Processing Toolbox.

Most face recognition systems tend to use either global image features, which describe an image as a whole, or local features, which represent image patches. Global features have the ability to generalize an entire object with a single vector. Consequently, their use in standard classification techniques is straightforward. Local features, on the other hand, are computed at multiple points in the image and are consequently more robust to occlusion and clutter. However, they may require specialized classification algorithms to handle cases in which there are a variable number of feature vectors per image. We have developed a new face recognition approach that combines both global and local features: this fast feature extraction method results extremally suitable for low computational power microprocessors.

The code has been tested with AT&T database achieving an excellent recognition rate of 97.84% (40 classes, 5 training images and 5 test images for each class, hence there are 200 training images and 200 test images in total randomly selected and no overlap exists between the training and test images).

Index Terms: Matlab, source, code, face, recognition, matching, global, local, features, feature, dimensionality, reduction.

Release 1.0 Date 2009.03.28
Major features:

Face Recognition . It Luigi Rosa mobile +39 3207214179 luigi.rosa@tiscali.it