.: Click here to download :.
Local photometric descriptors computed for interest
regions have proven to be very successful in applications
such as wide baseline matching, object
recognition, texture recognition, image
retrieval, robot localization, video data mining, building panoramas, and recognition of object
categories. They are distinctive, robust to
occlusion, and do not require segmentation. Recent work
has concentrated on making these descriptors invariant to
image transformations. The idea is to detect image regions
covariant to a class of transformations, which are then used
as support regions to compute invariant descriptors.
The fractional gaussian derivative can be computed in a number of ways, one such way is in the frequency domain.
Denoting the Fourier transform of the function f(x) as F(w), it is straight-forward to show that the Fourier
transform of the nth-order derivative, f(n)(x), is (jw)^n*F(w), for any integer order n. Of course,
there is no reason why n must be an integer, n can be any real (or complex) number -
hence the fractional derivative.
The code has been tested with AT&T
database achieving an excellent recognition rate of 99.60% (40 classes, 5
training images and 5 test images for each class, hence there are 200 training images and 200 test images in total
randomly selected and no overlap exists
between the training and test images).
Index Terms: Matlab, source, code, face recognition, webcam, local descriptors, web cam, fractional gaussian
derivatives, face matching, face identification.
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Figure 1. 2D Gaussian and Derivatives |
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A simple and effective source code for WebCam Face Identification. |
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Demo code (protected
P-files) available for performance evaluation. Matlab Image Processing Toolbox and Matlab Image Acquisition Toolbox are required. |
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Release |
Date |
Major features |
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2.0 |
2007.09.27 |
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1.0 |
2007.08.23 |
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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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WebCam Face Identification - Release 1.0 - Click here for
your donation. In order to obtain the source code you
have to pay a little sum of money: 600 EUROS (less
than 840 U.S. Dollars). |
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Once you have done this, please email us luigi.rosa@tiscali.it As soon as possible (in a few days) you will receive our new release of WebCam Face Identification. Alternatively, you can bestow using our banking coordinates:
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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 and Matlab Image Acquisition Toolbox are 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.
