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Source code for fingerprint recognition, face recognition and much more


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The theory of advanced correlation filters has evolved from the literature of optical pattern recognition in the last two decades; they have proved effective classifiers in a number of applications, among them biometric recognition and automatic target recognition. Correlation filter designs use the image intensity domain of training examples to compute a class template that produces characteristic correlation outputs to distinguish between authentic users and impostors. When applying thefilter for testing the authenticity of a new target image, the output plane is expected to have a shape containing a correlation peak if the image is authentic, but no such peak if the image belongs to another class. Properties of correlation filter classifiers include graceful degradation, shift invariance and closed-form solutions.

The code has been tested using fingerprint images taken with an UPEK swipe fingerprint reader with capacitive sensor and USB 2.0 connection. Database is 16 fingers wide and 8 impressions per finger deep (128 fingerprints in all). We have obtained the following results:

  • One-to-many fingerprint identification: using 2 images for each finger randomly selected for training and the remaining 6 images for testing (totally 32 images for training and 96 images for testing), without any overlapping, we have obtained an error rate smaller than 0.6% (top one error rate).
  • One-to-one fingerprint verification: we have obtained an EER equal to 5.6641%.


Index Terms: Matlab, source, code, correlation, filters, AFIS, automated, fingerprint, identification, system.

 

 

 

 

 

Figure 1. Fingerprint image



A simple and effective source code for Correlation Filters AFIS.



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

Release
Date
Major features
1.0

2011.05.27



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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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