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We propose a classification technique for face expression
recognition using AdaBoost that learns by selecting the relevant
global and local appearance features with the most
discriminating information. Selectivity reduces the dimensionality
of the feature space that in turn results in significant
speed up during online classification.
AdaBoost binary classification is generalized to a multiclass learning
algorithm simply by converting the discrete set of classes into a binary
string. The implemented system can automatically recognize seven expressions in real time that include anger, disgust, fear,
happiness, neutral, sadness and surprise. Experimental results are reported to show its potential applications in human computer interaction.
For the selection of most relevant features we have adopted the scheme
described in http://www.advancedsourcecode.com/adaboostoverfitting.asp.
From a set of N1 features we have selected top M1 features (M1<N1). To this subset we have added new features, so we have obtained
a new feature set of cardinality N2. Then from this set we have selected top M2
features (with M2<N2), and so on. This procedure has been repeated several
times until we have reached the desired recognition rate.
This code has been tested using the JAFFE Database, available at http://www.kasrl.org/jaffe.html. Using 150 images randomly selected for training
and 63 images for testing, without any overlapping, we obtain an excellent recognition rate greater than 84.29%. The semantic data ratings for this
database are available at http://www.kasrl.org/jaffe_info.txt.
Index Terms: Matlab, source, code, facial, expression, recognition, AdaBoost, multiclass.
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Figure 1. Facial expressions |
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A simple and effective source code for Adaboost Facial Expression Recognition. |
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Demo code (protected
P-files) available for performance evaluation. Matlab Image Processing Toolbox, Matlab Signal Processing Toolbox and Matlab Wavelet Toolbox are required. |
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Release |
Date |
Major features |
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1.0 |
2011.04.02 |
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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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Adaboost Facial Expression Recognition - 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 Adaboost Facial Expression Recognition. 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 Signal Processing Toolbox and Matlab Wavelet 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.
