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A novel biologically motivated face recognition algorithm based on polar
frequency is presented. Polar frequency descriptors are extracted from
face images by Fourier-Bessel transform (FBT).
Most of the current face recognition algorithms are based on feature extraction from a
Cartesian perspective, typical to most analog and digital imaging systems. The primate
visual system, on the other hand, is known to process visual stimuli logarithmically.
An alternative representation of an image in the polar frequency domain is the
two-dimensional Fourier-Bessel Transform. This transform found several applications in
analyzing patterns in a circular domain, but was seldom exploited for image recognition.
These results indicate the high informative value of the polar frequency content of face images
in relation to recognition and verification tasks, and that the Cartesian
frequency content can complement information about the subjects’ identity,
but possibly only when the images are not pre-normalized.
Yossi Zana and Roberto M. Cesar Jr, "Face recognition based on polar frequency features",
ACM Transactions on Applied Perception (TAP), Volume 3 Issue 1 (2006), pages 62-82.
Index terms: face recognition, fourier coefficients, polar frequency,
fourier-bessel transform, fbt, discrete fourier transform,
feature evaluation and selection, human perception.
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Figure 1. 2D Bessel function. |
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A simple and effective source code for Face Recognition Based on Polar Frequency Features. All tests were performed on AT&T database. |
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Demo code (protected
P-files) available for performance evaluation. Matlab
Image Processing Toolbox is required. |
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Release |
Date |
Major features |
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1.0 |
2006.01.17 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. This donation has to be considered an encouragement to improve the code itself. |
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Face Recognition Based on Polar Frequency Features - Release 1.0 - Click here for
your donation. In order to obtain the source code you
have to pay a little sum of money: 49 EUROS (less than
68,6 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 14 SP1. 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.
