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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.
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Figure 1. Gender recognition |
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A simple and effective source code for Gender Recognition System. |
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Demo code (protected
P-files) available for performance evaluation. Matlab Image Processing Toolbox and Matlab Signal Processing Toolbox are required.
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Release |
Date |
Major features |
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2.0 |
2010.12.28 |
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1.0 |
2009.12.26 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. This donation has to be considered an encouragement to improve the code itself. |
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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). |
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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 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 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.
