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

Semi-Automatic One-To-One Face Matching

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

The field of biometrics involves identifying people by measuring parts of their bodies. It now promises to find wide acceptance as a convenient and secure alternative to typed passwords, mechanical keys, or written signatures for access to computers, facilities or vehicles, and identification for financial transactions. Personal access control systems have been implemented using visual recognition for identification of individuals. Visual recognition systems use characteristic portions of the human body for identification purposes. Typical of this type of access control are face recognition systems and fingerprint recognition systems. Face recognition systems essentially operate by comparing some type of model image of a person's face (or representation thereof) to an image or representation of the person's face extracted from an input image. Image recognition, and particularly face recognition, is becoming an increasingly popular feature in a variety of applications. Face recognition applications can be used by security agencies, law enforcement agencies, the airline industry, the border patrol, the banking and securities industries and the like. Examples of potential applications include entry control to limited access areas, access to computer equipment, access to automatic teller terminals, identification of individuals and the like. In particular, security systems use face recognition to grant or deny access to select individuals, or to sound an alarm when a particular person is recognized, or to continually track an individual as the individual travels amongst a plurality of people, and so on. In like manner, home automation systems are being configured to distinguish among residents of a home, so that the features of the system can be customized for each resident.

We have developed a semi-automatic approach for one-to-one face matching that is capable to recognize an unknow input facial image. User has to manually select some fiducial points and within a fraction of a second a unique facecode is generated. Such binary code uniquely identifies a person.

Index Terms: Matlab, source, code, face, identification, one-to-one, 1:1, authentication, recognition, CPD, semi-automatic.

Release 1.0 Date 2009.07.29
Major features:

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