Face authentication using one-dimensional processing

Face authentication involves capturing the face images and representing the image suitable for matching with the reference template. In this paper, we discuss a new representation for matching that involves processing the image using 1-D processing that offers potential speed improvements over more conventional 2-D processing methods. Although the test application that is being considered here is to access a computer using face verification, this method can be used in other face verification applications. In this application, the subject is assumed to be cooperative, and the environment for capturing the face images is somewhat controlled. The proposed 1-D processing helps to locate the eyes, which in turn helps to normalize the face image for representation and matching. 1-D eigenanalysis is performed on the normalized face image to derive the eigenvectors. The face image is represented using components projected onto these eigenvectors. The 1-D PCA provides advantages over the conventional 2-D PCA in terms of providing a better model of the face in practical situations and providing robustness to local changes in the authentic images. We show that matching a test image with a reference image using the eigencomponents improves the discrimination between genuine and impostor face images. Our studies show good performance and it seems possible to obtain in practice an equal error rate (EER) close to zero.