In this paper, we present a new approach to enhance and improve the performance of automatic 3D face recognition system. The proposed method has been implemented through a preprocessing technique to align and normalise all images in the database based on eyes centres localisation using 2D normalised cross-correlation (2DNCC). Preprocessing 3D face data has
been implemented using depth map representation of the 3D data. The detected eyes centres and eyes distance (ED) have been used to segment and align 3D face images to produce a cropped face region of interest (ROI). The proposed approach extracted 3D face features using a set of selected orthogonal Gabor filters applied to normal map representation of the 3D face model. This approach minimises the feature vector extracted compared to systems that use complete Gabor filters bank. A further compression to the extracted features has been accomplished using linear discriminant analysis (LDA) before the
classification stage. Experimental results show that the proposed system is effective for both dimensionality reduction and good recognition performance when compared to current systems. The system has been tested using CASIA and Gavab 3D face images databases and achieved 98.35% and 85% recognition rates, respectively.