1. A face recognition method comprising: creating an SVM (support vector machine) classifier through machine learning on the basis of a degree of similarity between stored facial images in a database; normalizing a measured facial image to a predetermined size and subsequently dividing the normalized measured facial image along horizontal and vertical directions into at least six sub-divided images of the normalized measured facial image, wherein dividing the normalized measured facial image comprises: dividing the normalized measured facial image into three equal regions horizontally which respectively represent an eye region, a nose region, and a mouth region and which each have a height equal to ⅔ the distance between the eyes; and dividing the normalized measured facial image into three equal regions vertically which respectively represent a left eye region, a nose region, and a right eye region and which each have a width equal to ⅔ the distance between the eyes; extracting characteristic vectors from each of the sub-divided images of the normalized measured facial image and creating a similarity vector based on a degree of similiarity between registered characteristic vectors associated with at least one of the stored facial images and that of the extracted characteristic vectors of the normalized measured facial image, wherein the extracted and registered characteristic vectors are obtained by applying PCA (principal component analysis) to reduce the dimensions of data of images, and then applying LDA (linear discriminant analysis), wherein six extracted characteristic vectors are created from six sub-divided images of the normalized measured facial image; and inputting the similarity vector to the SVM classifier to perform authentication of the measured facial image.