P. Silapachote, D.R. Karuppiah, and A.R. Hanson (USA)
dimensional reduction, feature selection, pattern recognition, machine learning, AdaBoost, Support Vector Machine
We propose a classification technique for face expression recognition using AdaBoost that learns by selecting the rel evant global and local appearance features with the most discriminating information. Selectivity reduces the dimen sionality of the feature space that in turn results in signifi cant speed up during online classification. We compare our method with another leading margin-based classifier, the Support Vector Machines (SVM) and identify the advan tages of using AdaBoost over SVM in this context. We use histograms of Gabor and Gaussian derivative responses as the appearance features. We apply our approach to the face expression recognition problem where local appearances play an important role. Finally, we show that though SVM performs equally well, AdaBoost feature selection provides a final hypothesis model that can easily be visualized and interpreted, which is lacking in the high dimensional sup port vectors of the SVM.
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