Gabor filters for Face Recognition
We propose a biometric face recognition system based on local features. Informative feature locations in the face image are located by Gabor filters, which gives us an automatic system that is not dependent on accurate detection of facial features. The feature locations are typically located at positions with high information content (such as facial features), and at each of these positions we extract a feature vector consisting of Gabor coefficients.
Index Terms: face, recognition, Gabor filters, Gabor filtering, local features, Gabor coefficients, face matching, face recognition, face verification, feature vector.
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Figure 1. Facial image |
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A simple and effective source code for Face Recognition Based on Local 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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1.0
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2005.12.27
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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 Local 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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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 Local Features. 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 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% [?]
Facial Gender Recognition Using GA
Recognizing human gender plays an important role in many human computer interaction (HCI) areas. For example, search engines need an image filter to determine the gender of people in images from the Internet; demographic research can use gender information extracted from images to count the number of men and women entering a shopping mall or movie theater; a “smart building”might use gender for surveillance and control of access to certain areas. Besides these kinds of broad applications, gender recognition itself is an important research topic in both psychology and computer vision.
In psychology studies for HCI, the main focus is about how humans discriminate between males and females and what kind of features are more discriminative. A successful gender classification approach can boost the performance of many other applications including face recognition and smart human-computer interfaces. Despite its importance, it has received relatively little attention in the literature.
We have developed a system for facial gender recognition that is capable to extract from image most informative features using an approach based on genetic algorithms.
The code has been tested with Stanford Medical Student Face Database achieving an excellent recognition rate of 93.60% (200 female images and 200 male images, 90% used for training and 10% used for testing, hence there are 360 training images and 40 test images in total randomly selected and no overlap exists between the training and test images).
Index Terms: Matlab, source, code, gender, recognition, male, female, genetic, algorithm, algorithms, GA.
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Figure 1. Facial image |
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A simple and effective source code for Gender Recognition Based on Genetic Algorithms. |
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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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2010.05.18
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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Gender Recognition Based on Genetic Algorithms – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 200 EUROS (less than 280 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 Gender Recognition Based on Genetic Algorithms. 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 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% [?]
High Definition Image Compression Technology
The transport of images across communication paths is an expensive process. Image compression provides an option for reducing the number of bits in transmission. This in turn helps increase the volume of data transferred in a space of time, along with reducing the cost required. It has become increasingly important to most computer networks, as the volume of data traffic has begun to exceed their capacity for transmission. Traditional techniques that have already been identified for data compression include: Predictive coding, Transform coding and Vector Quantization. In brief, predictive coding refers to the decorrelation of similar neighbouring pixels within an image to remove redundancy. Following the removal of redundant data, a more compressed image or signal may be transmitted. Transform-based compression techniques have also been commonly employed. These techniques execute transformations on images to produce a set of coefficients. A subset of coefficients is chosen that allows good data representation (minimum distortion) while maintaining an adequate amount of compression for transmission. The results achieved with a transform-based technique is highly dependent on the choice of transformation used (cosine, wavelet, Karhunen-Loeve etc). Finally, vector quantization techniques require the development of an appropriate codebook to compress data. Usage of codebooks do not guarantee convergence and hence do not necessarily deliver infallible decoding accuracy. Also the process may be very slow for large codebooks as the process requires extensive searches through the entire codebook. Following the review of some of the traditional techniques for image compression, it is possible to discuss some of the more recent techniques that may be employed for data compression.
Artificial Neural Networks (ANNs) have been applied to many problems, and have demonstrated their superiority over traditional methods when dealing with noisy or incomplete data. One such application is for image compression. Neural networks seem to be well suited to this particular function, as they have the ability to preprocess input patterns to produce simpler patterns with fewer components. This compressed information (stored in a hidden layer) preserves the full information obtained from the external environment. Not only can ANN based techniques provide sufficient compression rates of the data in question, but security is easily maintained. This occurs because the compressed data that is sent along a communication line is encoded and does not resemble its original form. There have already been an exhaustive number of papers published applying ANNs to image compression. Many different training algorithms and architectures have been used. Some of the more notable in the literature are: nested training algorithms used with symmetrical multilayer neural networks, Self organising maps, for codebook generation, principal component analysis networks, backpropagation networks, and the adaptive principal component extraction algorithm. Apart from the existing technology on image compression represented by series of JPEG,MPEG and H.26x standards, new technology such as neural networks and genetic algorithms are being developed to explore the future of image coding. Successful applications of neural networks to vector quantization have now become well established, and other aspects of neural network involvement in this area are stepping up to play significant roles in assisting with those traditional technologies.
Index Terms: Matlab, source, code, neural networks, image compression, image processing, image reconstruction, codebook, quantization.
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Figure 1. Compressed image |
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A simple and effective source code for Image Compression With Neural Networks. |
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Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox, Matlab Communications Toolbox and Matlab Neural Network Toolbox are required.
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1.0
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2008.10.17
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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Image Compression With Neural Networks – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 95 EUROS (less than 133 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 Image Compression With Neural Networks. 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 2006a. Matlab Image Processing Toolbox, Matlab Communications 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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