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% [?]
Neural Network Technology
Having an easier life by the help of developing technologies forces people is more complicated technological structure. In today’s world, security is more important than ever. Dazziling developments in technology arouse interest of scientists about human and human behaviors and at the same time, give an opportunity to people to apply their thoughts. Today, for security needs, detailed researches are organized to set up the most reliable system. Iris Recognition Security System is one of the most reliable leading technologies that most people are related. Iris recognition technology combines computer vision, pattern recognition, statistical inference, and optics. Its purpose is real-time, high confidence recognition of a person’s identity by mathematical analysis of the random patterns that are visible within the iris of an eye from some distance. Because the iris is a protected internal organ whose random texture is stable throughout life, it can serve as a kind of living passport or a living password that one need not remember but can always present. Because the randomness of iris patterns has very high dimensionality, recognition decisions are made with confidence levels high enough to support rapid and reliable exhaustive searches through national-sized databases.
Artificial Neural Networks (ANNs) are programs designed to simulate the way a simple biological nervous system is believed to operate. They are based on simulated nerve cells or neurons, which are joined together in a variety of ways to form networks. These networks have the capacity to learn, memorize and create relationships amongst data. ANN is an information-processing paradigm, implemented in hardware or software that is modeled after the biological processes of the brain. An ANN is made up of a collection of highly interconnected nodes, called neurons or processing elements. A node receives weighted inputs from other nodes, sums these inputs, and propagates this sum through a function to other nodes. This process is analogous to the actions of a biological neuron. An ANN learns by example. In a biological brain, learning is accomplished as the strengths of the connections between nodes are adjusted. This is true for ANN’s also, as these strengths are captured by the weights between the nodes. ANN’s most important advantage is that they can be used to solve problems of considerable complexity; problems that do not have an algorithmic solution or for which such a solution is too complex to be found. Because of their abstraction from the brain, ANNs are good at solving problems that humans are good at solving but which computers are not. Pattern recognition and classification are examples of problems that are well suited for ANN application.
Index Terms: Matlab, source, code, iris, recognition, segmentation, detection, verification, matching, ann, nn, neural, network, networks.
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Figure 1. Neural network example |
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A simple and effective source code for Personal Iris Recognition Using Neural Network. |
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Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox, Matlab Neural Network Toolbox and Matlab Signal Processing Toolbox are required.
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1.0
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2008.12.15
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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Personal Iris Recognition Using Neural Network – 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 Personal Iris Recognition Using Neural Network. 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 Neural Network Toolbox and Matlab Signal Processing 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% [?]
Optimal Features Extraction Using Genetic Algorithms for Iris Recognition
There has been a rapid increase in the need of accurate and reliable personal identification infrastructure in recent years, and biometrics has become an important technology for the security. Iris recognition has been considered as one of the most reliable biometrics technologies in recent years. The human iris is the most important biometric feature candidate, which can be used for differentiating the individuals. For systems based on high quality imaging, a human iris has an extraordinary amount of unique details. Features extracted from the human iris can be used to identify individuals, even among genetically identical twins. Iris-based recognition system can be noninvasive to the users since the iris is an internal organ as well as externally visible, which is of great importance for the real-time applications.
We have developed an iris recognition method based on genetic algorithms (GA) for the optimal features extraction. The accurate iris patterns classification has become a challenging issue due to the huge number of textural features extracted from an iris image with comparatively a small number of samples per subject. The traditional feature selection schemes like principal component analysis, independent component analysis, singular valued decomposition etc. require sufficient number of samples per subject to select the most representative features sequence; however, it is not always realistic to accumulate a large number of samples due to some security issues. We propose GA to improve the feature selection by optimal filtering.
This code is based on Libor Masek’s excellent implementation available here.
Libor Masek, Peter Kovesi. MATLAB Source Code for a Biometric Identification System Based on Iris Patterns. The School of Computer Science and Software Engineering, The University of Western Australia, 2003.
All tests were performed with CASIA Iris Image Database available at http://www.cbsr.ia.ac.cn/IrisDatabase.htm.
Index Terms: Matlab, source, code, iris, recognition, matching, GA, genetic, algorithms, algorithm.
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Figure 1. Genetic sequence |
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A simple and effective source code for Iris 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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Date
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Major features
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1.0
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2010.05.05
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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Iris 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: 300 EUROS (less than 420 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 Iris 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% [?]
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