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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 64×64 pixels, with 256 grey levels per pixel. A 5×5 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.

 

 

 

 

 

Figure 1. Facial image



A simple and effective source code for GA MACE Face Recognition.



Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox is required.

Release
Date
Major features
1.0

2011.07.08



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GA MACE Face Recognition - Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 400 EUROS (less than 560 U.S. Dollars).

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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 2006a. Matlab Image Processing Toolbox is required. The code provided has to be considered "as is" and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.

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