Face Recognition Based on Fractional Gaussian Derivatives
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
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Date
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Major features
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2.0
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2007.09.27 |
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1.0
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2007.08.23 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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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.
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