Concerning the credibility problem of the classification results in face recognition, a face recognition method based on the theory of uncertainty was proposed. Firstly, in order to estimate 3D features, Active Appearance Model (AAM) and triangulation were used to process two 2D images of unknown object. Then, the score of each object in the database was estimated, and two images were further processed through uncertainty. Finally, the decision was made based on the estimated scores and the estimated uncertainty classification list. All identified objects and their corresponding credibilities were stored in the classification list. Stereo vision system with two cameras captures face images of various postures in the experiment. Compared with a similar probability forecasting measurement method, the correct detection rate of the proposed method was increased by 10%, and the false detection rate was reduced by at least 9%. The experimental results show that the classification accuracy is improved by constructing the uncertainty information of 3D image feature and adopting appropriate statistical method.