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

Statistical Moments for Pattern Recognition

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

Moment based feature descriptors have evolved into a powerful tool for image analysis applications. Geometric moments present a low computational cost, but are highly sensitive to noise. Furthermore reconstruction is extremely difficult. Although not invariant under rotation, Hu's invariants that are derived from geometric moments present invariance under linear transformations. Complex moments provide with additional invariant descriptors, but present the same problems regarding noise and reconstruction. Moments of orthogonal polynomial basis were proposed by Teague. They have proven less sensitive to noise, are natively invariant to linear transformations and can be effectively used for image reconstruction. Computational complexity, however, becomes a major issue, and real-time implementation in software has not been reported. Moments of discrete orthogonal basis have been proposed recently. They are fast to implement, present adequate noise tolerance and very accurate image reconstruction. Their major drawback is the lack of invariance under transformation. Image normalization should be used prior to moment extraction for applications requiring invariance. We have developed a simple and efficient technique for face recognition that combines:

  • Centralised moments
  • Normalised moments
  • Hu invariant moments
  • Legendre moments

Each feature vector in fact is not discriminative for identification and only using them all at once with appropriate weights it is possible to reach an excellent recognition rate.

Index Terms: Matlab, source, code, face, recognition, statistical, moments, moment, invariant, Hu, centralised, Legendre.

Release 1.0 Date 2008.07.15
Major features:

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