The category information of the -nearest neighbor labeled samples is used

but the contribution of the test samples is omitted in the weighted -nearest neighbor method

which often lead to misclassifications. Aimed at the problem

a semi-supervised -nearest neighbor method is proposed in this paper. The method can classify sequential samples and non-sequential samples better than the -nearest neighbor method. In the decision process of classification

the information of c-nearest neighbor samples in the test set is used. So

classification accuracy is improved. The recognition accuracy of the method is 5.95% higher for sequential images in Cohn-Kanade face database

and 7.89% higher for non-sequential images in Cohn-Kanade face database than it of weighted -nearest neighbor method. The experiment shows that the method performs fast and has high classification accuracy.