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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.
Figure 1. Facial expressions
A simple and effective source code for Adaboost Facial Expression Recognition.
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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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
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