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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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.
Popularity: 1% [?]
High Speed Face Recognition Based on Discrete Cosine Transforms and Neural Networks
High information redundancy and correlation in face images result in inefficiencies when such images are used directly for recognition. In this paper, discrete cosine transforms are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth. We demonstrate experimentally that when DCT coefficients are fed into a backpropagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. This makes DCT-based face recognition much faster than other approaches.
Zhengjun Pan and Hamid Bolouri, “High Speed Face Recognition Based on Discrete Cosine Transforms and Neural Networks”, 1999.
Index Terms: Face recognition, neural networks, feature extraction, discrete cosine transform, face matching, face identification, dct, ann, artificial neural networks, nn.
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Figure 1. Architecture of neural networks |
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A simple and effective source code for Face Identification based on DCT and Neural Networks. All tests were performed with AT&T face database available here. A complete list of public face databases is available at http://www.advancedsourcecode.com/facedatabase.asp. |
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Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox and Matlab Neural Network Toolbox are required.
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Release
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Date
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Major features
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1.0
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2006.05.16 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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DCT-ANN Based Face Recognition System – Release 1.0 – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 30 EUROS (less than 42 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 DCT-ANN Based Face Recognition System. 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 Neural Network 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.
Popularity: 1% [?]
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