Global face recognition methods based on projection (such as principal component analysis) exhibit a well-known sensitivity to face pitch and yaw variations. This paper introduces and tests a new approach to the normalization of "off-frontal" face images and applies it in a principal component analysis (PCA) framework. Our proposed normalization employs two affine transformations of triangular face regions to register selected facial features. Experiments with the technique demonstrate performance improvements over a traditional normalization method when the proposed normalization step is employed. This experiment reports the results based on an image data set containing 665 images of 269 subjects photographed with noticeable face yaw, plus up to 1100 additional training images of subjects with frontal face pose.