In this paper, we present a novel automatic approach based on local shape descriptors to discriminate 3-D facial scans of different individuals. Our approach begins with registration, smoothing and uniform resampling of 3-D face data. Then, uniformly resampled 3-D face data are used to generate shape index, curvedness, gaussian and mean curvature values on each point of the data. Hence we obtain 2-D matrices of shape index, curvedness, gaussian and mean curvature values representing 3-D geometry information. SIFT descriptors are applied to 2-D matrices and high dimensional feature vector having shape information is obtained. Finally, high dimensional feature vector is projected to the low dimensional subspace where projection matrix is calculated by linear discriminant analysis. Features in this low dimensional subspace are compared by using cosine distance similarity metric. Proposed method is shown to have 98.35% and 98.25% detection rates at 0.001 false alarm rate for All vs. All and ROC3 experiments respectively on FRGC v2.0 database. To the best of our knowledge, these are the best results among similar studies available in 3-D face recognition literature.