This paper proposes a scheme that offers robust extraction of target images in standard view from input facial images, in order to realize accurate and automatic identification of human faces. A standard view for target images is defined using internal facial features, i.e., the two eyes and the mouth, as steady reference points of the human face. Because reliable detection of such facial features is not an easy task in practice, the proposed scheme is characterized by a combination of two steps: first, all possible regions of facial features are extracted using a color image segmentation algorithm, then the target image is selected from among the candidates defined by tentative combination of the three reference points, through applying the classification framework using the sub-space method. Preliminary experiments on the scheme's flexibility based on subjective assessment indicate a stability of nearly 100% in consistent extraction of target images in the standard view, not only for familiar faces but also for unfamiliar faces, when the input face image roughly matches the front view. By combining this scheme for normalizing images into the standard view with an image matching technique for identification, an experimental system for identifying faces among a limited number of subjects was implemented on a commercial engineering workstation. High success rates achieved in the identification of front view face images obtained under uncontrolled conditions have objectively confirmed the potential of the scheme for accurate extraction of target images.