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 Fractional Gaussian Derivatives

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

Local photometric descriptors computed for interest regions have proven to be very successful in applications such as wide baseline matching, object recognition, texture recognition, image retrieval, robot localization, video data mining, building panoramas, and recognition of object categories. They are distinctive, robust to occlusion, and do not require segmentation. Recent work has concentrated on making these descriptors invariant to image transformations. The idea is to detect image regions covariant to a class of transformations, which are then used as support regions to compute invariant descriptors.

The fractional gaussian derivative can be computed in a number of ways, one such way is in the frequency domain. Denoting the Fourier transform of the function f(x) as F(w), it is straight-forward to show that the Fourier transform of the nth-order derivative, f(n)(x), is (jw)^n*F(w), for any integer order n. Of course, there is no reason why n must be an integer, n can be any real (or complex) number - hence the fractional derivative.

The code has been tested with AT&T database achieving an excellent recognition rate of 99.60% (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, webcam, local descriptors, web cam, fractional gaussian derivatives, face matching, face identification.

Release 2.0 Date 2007.09.27
Major features:
Release 1.0 Date 2007.08.23
Major features:
  • Face recognition based on fractional gaussian derivatives
  • High recognition rate: 99.40% using AT&T Database
  • Easy and intuitive GUI
  • Command line functions for rapid testing
  • Webcam image acquisition

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