In our previously developed method for the facial expression recognition of a speaker, the positions of feature vectors in the feature vector space in image processing were generated with imperfections. The imperfections, which caused misrecognition of the facial expression, tended to be far from the center of gravity of the class to which the feature vectors belonged. In the present study, to omit the feature vectors generated with imperfections, a method using reject criteria in the feature vector space was applied to facial expression recognition. Using the proposed method, the facial expressions of two subjects were discriminable with 86.8 % accuracy for the three facial expressions of “happy”, “neutral”, and “others” when they exhibited one of the five intentional facial expressions of “angry”, “happy”, “neutral”, “sad”, and “surprised”, whereas these expressions were discriminable with 78.0 % accuracy by the conventional method. Moreover, the proposed method effectively judged whether the training data were acceptable for facial expression recognition at the moment.