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

3D Technologies for Face Recognition

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

Human target recognition has been an active research area in the last years, with a major emphasis on automatic detection and matching of faces in still images and videos, for the purposes of verification and identification. Performance of 2D face matching systems depends on their capability of being insensitive to critical factors such as facial expressions, makeup and aging, but mainly hinges upon extrinsic factors such as illumination differences, camera viewpoint and scene geometry. However, the inherent limitations of 2D face matching have supported the belief that effective recognition of identity should be obtained through multi-biometric technologies. In particular, the exploitation of the geometry of the anatomical structure of the face rather than its appearance, with definition of algorithms and systems for 3D face matching has been a growing field of research in the very recent years. 3D Face recognition systems aim to use the additional 3D data to eliminate some of the intrinsic problems associated with 2D recognition systems. For example, the 3D surface of a face is invariant to changes in lighting conditions and hence recognition systems that use this data should be, by definition, illumination invariant. Furthermore, given that it is possible to register a number of 3D models to a base pose, such a system would also be viewpoint invariant (although to what degree depends on the completeness of the 3D head model). In addition to the 3D data it remains possible to capture texture information and thus use all the available data to guide the recognition process.

Code has been tested on GavabDB Database. GavabDB is a 3D face database. It contains 549 three-dimensional images of facial surfaces. These meshes correspond to 61 different individuals (45 male and 16 female) having 9 images for each person. The total of the individuals are Caucasian and their age is between 18 and 40 years old. Each image is given by a mesh of connected 3D points of the facial surface without texture. The database provides systematic variations with respect to the pose and the facial expression.

Index Terms: Matlab, source, code, 3D, face, recognition, verification, model, matching, virtual, reality, modeling, language, vrml.

Release 1.0 Date 2012.06.11
Major features:

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