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% [?]
Text-Independent Speaker Recognition Based on Neural Networks
Speaker recognition or voice recognition is the task of recognizing people from their voices. Such systems extract features from speech, model them and use them to recognize the person from his/her voice. Speaker recognition has a history dating back some four decades, where the output of several analog filters was averaged over time for matching. Speaker recognition uses the acoustic features of speech that have been found to differ between individuals. These acoustic patterns reflect both anatomy (e.g., size and shape of the throat and mouth) and learned behavioral patterns (e.g., voice pitch, speaking style). This incorporation of learned patterns into the voice templates (the latter called “voiceprints”) has earned speaker recognition its classification as a “behavioral biometric.”
Speaker recognition systems employ three styles of spoken input: text-dependent, text-prompted and text-independent. Most speaker verification applications use text-dependent input, which involves selection and enrollment of one or more voice passwords. Text-prompted input is used whenever there is concern of imposters. The various technologies used to process and store voiceprints includes hidden Markov models, pattern matching algorithms, neural networks, matrix representation and decision trees. Some systems also use “anti-speaker” techniques, such as cohort models, and world models. Ambient noise levels can impede both collection of the initial and subsequent voice samples. Performance degradation can result from changes in behavioral attributes of the voice and from enrollment using one telephone and verification on another telephone. Voice changes due to aging also need to be addressed by recognition systems.
Many companies market speaker recognition engines, often as part of large voice processing, control and switching systems. Capture of the biometric is seen as non-invasive. The technology needs little additional hardware by using existing microphones and voice-transmission technology allowing recognition over long distances via ordinary telephones (wire line or wireless). Multi-layered networks are capable of performing just about any linear or nonlinear computation, and can approximate any reasonable function arbitrarily well. Such networks overcome the problems associated with the perceptron and linear networks. However, while the network being trained may be theoretically capable of performing correctly, back propagation and its variations may not always find a solution. There are many types of neural networks for various applications multilayered perceptrons (MLPs) are feedforward networks and universal approximators. They are the simplest and therefore most commonly used neural network architectures.
Index Terms: Matlab, speaker recognition, speaker verification, speaker matching, neural networks, feature extraction, ann, artificial neural networks, nn.
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Figure 1. Speech signal |
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A simple and effective source code for Speaker Identification based on Neural Networks. |
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Demo code (protected P-files) available for performance evaluation. Matlab Signal Processing Toolbox and Matlab Neural Network Toolbox are required.
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1.1
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2006.07.12
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1.0
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2006.06.14
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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Speaker Recognition System Based on ANN – Release 1.0 – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 150 EUROS (less than 210 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 Speaker Recognition System Based on ANN. Alternatively, you can bestow using our banking coordinates:
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This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
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 Signal 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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