Title: 3D face recognition based on normal map features using selected Gabor filters and linear discriminant analysis
Authors: Samir F. Hafez; Mazen M. Selim; Hala H. Zayed
Addresses: Faculty of Engineering, Benha University, Benha, Egypt ' Department of Computer Science, Faculty of Computers and Informatics, Benha University, Benha, Egypt ' Department of Computer Science, Faculty of Computers and Informatics, Benha University, Benha, Egypt
Abstract: 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.
Keywords: 3D modelling; face recognition; preprocessing; normal maps; depth maps; linear discriminant analysis; LDA; Gabor filters; eye centre localisation; eye distance; face images; feature extraction; biometrics.
International Journal of Biometrics, 2015 Vol.7 No.4, pp.373 - 390
Received: 15 Sep 2015
Accepted: 26 Feb 2016
Published online: 26 Apr 2016 *