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 Based on Genetic Algorithms For Feature Correlation

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

Human face recognition is currently a very active research area with focus on ways to perform robust and reliable biometric identification. Face recognition, the art of matching a given face to a database of known faces, is a non-intrusive biometric method that dates back to the 1960s. In efforts going back to far earlier times, people have tried to understand which facial features help us perform recognition tasks, such as identifying a person, deciding on an individual's age and gender, and classifying facial expression and even beauty. A recognition system has to be invariant both to external changes, like environmental light, partial occlusions and the person's position and distance from the camera, and internal deformations, like facial expression and aging. Because most commercial applications use large databases of faces, recognition systems have to be computationally efficient. We have developed a code to perform face identification using a Genetic algorithm-optimized Minimum Average Correlation Energy (MACE) filtering technique. The performances of the proposed algorithm are evaluated using Facial Expression Database collected at the Advanced Multimedia Processing Lab at Carnegie Mellon University (CMU). Database consists of 13 subjects, each with 75 images. The size of each image is 6464 pixels, with 256 grey levels per pixel. A 55 filter has been designed using genetic algorithms. With GA Feature Correlation we have achieved an EER equal to 3.70%.

Index Terms: Matlab, source, code, correlation, filters, face, recognition, identification, system, MACE, GA, genetic, algorithm.

Release 1.0 Date 2011.07.08
Major features:

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