Fusion of feature sets and classifiers for facial expression recognition

This paper presents a novel method for facial expression recognition that employs the combination of two different feature sets in an ensemble approach. A pool of base support vector machine classifiers is created using Gabor filters and Local Binary Patterns. Then a multi-objective genetic algorithm is used to search for the best ensemble using as objective functions the minimization of both the error rate and the size of the ensemble. Experimental results on JAFFE and Cohn-Kanade databases have shown the efficiency of the proposed strategy in finding powerful ensembles, which improves the recognition rates between 5% and 10% over conventional approaches that employ single feature sets and single classifiers.

► A novel method for facial expression recognition that employs an ensemble of classifiers.
► A pool of base SVM classifiers is created using Gabor filters and LBP as feature sets.
► A multi-objective genetic algorithm is used to search for the best ensemble.
► Experiments on JAFFE and Cohn-Kanade databases show the efficiency of the proposed method.