The face recognition method is proposed for cases of an insufficient training set, when the input data consists only of two facial images (full face and profile). The 3D model of a face is created semi-automatically using the input data (two images), which is then used for the recognition process. The training set for the recognition process consists of these created 3D models of faces. The basic problem of face recognition is the insufficient information about the proportions of the unidentified person's face, images can also contain some artefacts, for example eyeglasses, beard, moustache that can decrease the precision of the recognition process and make the image analysis more difficult. Another important aspect is illumination, which can practically change the results of the classification. The proposed recognition method consists of several steps: unknown image face alignment, facial reference points estimation using gradient maps using dlib (http://dlib.net/) and OpenCV (https://opencv.org/) open source computer vision libraries. After features extraction it is necessary to perform thresholding on some facial reference points, which is most important for recognition process. For this purpose, several important features are selected and distances between them are calculated. The training set consists of early created 3D models of faces that could be used to get the missing information about the proportions of the person's face. The proposed algorithm is used for classification. Using this method classification results are approximately 90% positive compared to when using only the insufficient training set that contains only two images.