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Correlation Filters AFIS

By Luigi Rosa Free

Developer's Description

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 the filter 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.

Full Specifications

What's new in version 1.0


Release November 21, 2012
Date Added November 21, 2012
Version 1.0

Operating Systems

Operating Systems Windows 2003, Windows Vista, Windows 98, Windows Me, Windows, Windows NT, Windows 2000, Windows 8, Windows Server 2008, Windows 7, Windows XP
Additional Requirements Matlab


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