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Correlation filters have been applied successfully to automatic target recognition (ATR) problems. The most basic correlation filter is the matched spatial filter (MSF), whose impulse response (in 2-D, point spread function) is the flipped version of the reference image. While the MSF performs well at detecting a reference image corrupted by additive white noise, it performs poorly when the reference image appears with distortions (e.g., rotations, scale changes). Thus one MSF will be needed to detect each appearance of an object. Clearly this is computationally unattractive for practical pattern recognition. Hester and Casasent addressed this challenge with the introduction of the synthetic discriminant function (SDF) filter. The SDF filter is a linear combination of MSFs where the combination weights are chosen so that the correlation outputs corresponding to the training images would yield pre-specified values at the origin. These pre-specified peak values are often referred to as peak constraints. The peak values corresponding to the authentics (also called the true class) are typically set to 1, and hence this SDF filter was known as the equal correlation peak (ECP) SDF filter. In principle, a single ECP SDF filter could replace many MSFs. Object recognition is performed by cross-correlating an input image with a synthesized template or filter and processing the resulting correlation output. The correlation output is searched for peaks, and the relative heights of these peaks are used to determine whether the object of interest is present or not. The locations of the peaks indicate the position of the objects.

Face verification is an important tool for authentication of an individual and it can be of significant value in security and e-commerce applications. We have developed an effective application of correlation filters for face verification. The performance of a specific type of correlation filter called the minimum average correlation energy (MACE) filter is evaluated using Facial Expression Database collected at the Advanced Multimedia Processing Lab at Carnegie Mellon University (CMU).

Index Terms: Matlab, source, code, face, identification, authentication, recognition, correlation, filters, filter, mace.

 

 

 

 

Figure 1. Correlation filters



A simple and effective source code for Correlation Filters Face Verification.

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

Release
Date
Major features
2.0

2011.12.07

  • Minor bug fixed
1.0

2010.08.10



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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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