Fast and Accurate Face Identification Using Overlapping DCT
In the JPEG image compression algorithm, the input image is divided into 8-by-8 or 16-by-16 blocks, and the two-dimensional DCT is computed for each block. The DCT coefficients are then quantized, coded, and transmitted. The JPEG receiver (or JPEG file reader) decodes the quantized DCT coefficients, computes the inverse two-dimensional DCT of each block, and then puts the blocks back together into a single image. For typical images, many of the DCT coefficients have values close to zero; these coefficients can be discarded without seriously affecting the quality of the reconstructed image. Such algorithm results particularly robust also for face identification. Moreover the 2D DCT operator can be applied to overlapping data.
The extracted feature vectors are used as input to a simple nearest neighbor algorithm. The k-nearest neighbor algorithm is amongst the simplest of all machine learning algorithms. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors. k is a positive integer, typically small. If k = 1, then the object is simply assigned to the class of its nearest neighbor. In binary (two class) classification problems, it is helpful to choose k to be an odd number as this avoids difficulties with tied votes. The same method can be used for regression, by simply assigning the property value for the object to be the average of the values of its k nearest neighbors. It can be useful to weight the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. The neighbors are taken from a set of objects for which the correct classification (or, in the case of regression, the value of the property) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required. In order to identify neighbors, the objects are represented by position vectors in a multidimensional feature space. It is usual to use the Euclidean distance, though other distance measures, such as the Manhattan distance could in principle be used instead. The k-nearest neighbor algorithm is sensitive to the local structure of the data.
The code has been tested with AT&T database achieving an excellent recognition rate of 99.20% (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, face matching, face verification, dct, k-nearest neighbor algorithm, knn, discrete cosine transform.
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Figure 1. Example of k-NN classification |
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A simple and effective source code for Face Recognition. |
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Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox is required. |
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1.0
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2007.10.20 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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Face Recognition Based On Overlapping DCT – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 250 EUROS (less than 350 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 Face Recognition Based On Overlapping DCT. 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 Release 2006a. 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.
Popularity: 1% [?]
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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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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