Facial Gender Recognition Using AdaBoost
Human face contains a variety of information for adaptive social interactions amongst people. In fact, individuals are able to process a face in a variety of ways to categorize it by its identity, along with a number of other demographic characteristics, such as gender, ethnicity, and age. In particular, recognizing human gender is important since people respond differently according to gender. In addition, a successful gender classification approach can boost the performance of many other applications, including person recognition and smart human-computer interfaces.
We have developed an algorithm for gender recognition based on AdaBoost algorithm. Boosting has been proposed to improve the accuracy of any given learning algorithm. In Boosting one generally creates a classifier with accuracy on the training set greater than an average performance, and then adds new component classifiers to form an ensemble whose joint decision rule has arbitrarily high accuracy on the training set. In such a case, we say that the classification performance has been “boosted”. In overview, the technique train successive component classifiers with a subset of the entire training data that is “most informative” given the current set of component classifiers. AdaBoost (Adaptive Boosting) is a typical instance of Boosting learning. In AdaBoost, each training pattern is assigned a weight that determines its probability of being selected for some individual component classifier. Generally, one initializes the weights across the training set to be uniform. In the learning process, if a training pattern has been accurately classified, then its chance of being used again in a subsequent component classifier is decreased; conversely, if the pattern is not accurately classified, then its chance of being used again is increased.
The code has been tested with Stanford Medical Student Face Database achieving an excellent recognition rate of 89.61% (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, identification, adaboost, male, female.
 |
Figure 1. Gender recognition |
||||||||||||||
|
A simple and effective source code for Gender Recognition System. |
|||||||||||||||
 |
Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox and Matlab Signal Processing Toolbox are required.
|
||||||||||||||
|
Release
|
Date
|
Major features
|
|||||||||||||
|
1.0
|
2009.12.26
|
|
|||||||||||||
|
We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
|||||||||||||||
|
Gender Recognition System. 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).
|
|||||||||||||||
|
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 System. Alternatively, you can bestow using our banking coordinates:
|
|||||||||||||||
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 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% [?]
Off-Line Signature Recognition
There exist a number of biometrics methods today e.g. Signatures, Fingerprints, Iris etc. There is considerable interest in authentication based on handwritten signature verification system as it is the cheapest way to authenticate the person. Fingerprints and Iris verification require the installation of costly equipments and hence can not be used at day to day places like Banks etc. As because Forensic experts can not be employed at every place, there has been considerable effort towards developing algorithms that could verify and authenticate the individual’s identity . Many times the signatures are not even readable by human beings. Therefore a signature is treated as an image carrying a certain pattern of pixels that pertains to a specific individual. Signature Verification Problem therefore is concerned with determining whether a particular signature truly belongs to a person or not.
Signatures are a special case of handwriting in which special characters and flourishes are viable. Signature Verification is a difficult pattern recognition problem as because no two genuine signatures of a person are precisely the same. Its difficulty also stems from the fact that skilled forgeries follow the genuine pattern unlike fingerprints or irises where fingerprints or irises from two different persons vary widely. Ideally interpersonal variations should be much more than the intrapersonal variations. Therefore it is very important to identify and extract those features which minimize intrapersonal variation and maximize interpersonal variations. There are two approaches to signature verification, online and offline differentiated by the way data is acquired. In offline case signature is obtained on a piece of paper and later scanned. While in online case signature is obtained on an electronic tablet and pen. Obviously dynamic information like speed, pressure is lost in offline case unlike online case.
Code has been tested using Off line signature database, Grupo de Procesado Digital de Señales, available at http://www.gpds.ulpgc.es/download/index.htm.
Index Terms: Matlab, source, code, signature recognition, off-line, on-line, verification, identification.
 |
Figure 1. Signature |
||||||||||||||
|
A simple and effective source code for Off-Line Signature Recognition. |
|||||||||||||||
|
|
Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox is required.
|
||||||||||||||
|
Release
|
Date
|
Major features
|
|||||||||||||
|
1.0
|
2008.04.13 |
|
|||||||||||||
|
We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
|||||||||||||||
|
Signature Recognition System – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 120 EUROS (less than 168 U.S. Dollars).
|
|||||||||||||||
|
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 Signature Recognition System. Alternatively, you can bestow using our banking coordinates:
|
|||||||||||||||
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: 2% [?]
Coherent Point Drift for Biometric Identification: Ear Recognition
Biometrics is being more and more widely used in recently year owing to the irreproducible characteristics of the human body. As one kind of biometrics, the ear has its own characters: the structure of the ear is rich and stable, and does not change radically over time; the ear is less variability with expressions, and has a more uniform distribution of color than faces. These unique characters of the ear make it possible to make up the drawbacks of other biometrics and to enrich the biometrics identification technology. At present ear recognition technology has been developed from the initial feasible research to the stage of how to enhance ear recognition performance further, for instance, 3D ear recognition, ear recognition with occlusion, and multi-pose ear recognition.
We have developed a simple and fast algorithm for ear recognition based on Principal Component Analysis that is capable to recognize ears with a low error rate. Moreover code is capable to perform 1:1 verification using an approach based on Coherent Point Drift (CPD) with a high degree of accuracy. The code for CPD has been developed by Andriy Myronenko and it is available at http://www.bme.ogi.edu/~myron/matlab/cpd/. CPD is an excellent Matlab toolbox for rigid, affine and non-rigid point set registration and matching and allows to align two N-D point sets and recover the correspondences.
The proposed algorithm has been tested on USTB Ear Image Databases, using Dataset #1, that includes 185 ear images of 60 persons.
Index Terms: Matlab, source, code, ear, recognition, identification, matching, CPD, coherent, point, drift, PCA.
 |
Figure 1. Ear |
||||||||||||||
|
A simple and effective source code for Ear Recognition System. |
|||||||||||||||
|
|
Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox is required.
|
||||||||||||||
|
Release
|
Date
|
Major features
|
|||||||||||||
|
1.0
|
2009.07.15 |
|
|||||||||||||
|
We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
|||||||||||||||
|
Ear Recognition System. 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).
|
|||||||||||||||
|
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 Ear Recognition System. Alternatively, you can bestow using our banking coordinates:
|
|||||||||||||||
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% [?]
Translator
Follow Us
Video Posts
