An approach is proposed to recognize the facial expression using Gabor feature and Adaboost.Since the high-dimensional Gabor feature vectors are quite redundant,Adaboost is introduced as a method of features selection.Furthermore,combined with the nearest distance classifier,the support vector machine(SVM) is used for classification.This approach takes the advantages of the favorable ability of Gabor feature in representing expression variability,the effective function of Adaboost in feature selection,and the high performance of SVM in the solution to small sample size,high dimension problems.Experiments with JAFFE show that the approach is quite effective.Meanwhile,the feature set selected by Adaboost also indicates that the features extracted from eye and mouth regions play the most important role in expression recognition.