Automatic 3D Face Recognition Using Fourier Descriptors Eyad Elyan School of Computing, Robert Gordon University Aberdeen, AB251HG, UK Email: e.elyan@rgu.ac.uk Hassan Ugail School of Informatics University of Bradford Bradford, BD71DP, UK h.ugail@brad.ac.uk Abstract—3D face recognition is attracting more attention due to the recent development in 3D facial data acquisition techniques. It is strongly believed that 3D Face recognition systems could overcome the inherent problems of 2D face recognition such as facial pose variation, illumination, and variant facial expression. In this paper we present a novel technique for 3D face recognition system using a set of parameters representing the central region of the face. These parameters are essentially vertical and cross sectional profiles and are extracted automatically without any prior knowledge or assumption about the image pose or orientation. In addition, these profiles are stored in terms of their Fourier Coefficients in order to minimize the size of input data. Our approach is validated and verified against two different datasets of 3D images covers enough systematic and pose variation. High recognition rate was achieved. Keywords-2D Face Recognition; 3D Face Recognition; 3D images I. I NTRODUCTION Most of the research work done on the area of face recognition was based on 2D representation of facial data, hence a wide range of 2D algorithm are available in the literature [1]. 3D face recognition is attracting more attention in the recent years due to two important factors. Firstly, because of the inherent problems with 2D face recognition system that appears to be very sensitive to facial pose variation, variant facial expression, and lighting and illumination. For more information about current work in the 3D recognition area see [2], [3]. Secondly, due to the recent development in the 3D acquisition techniques such as 3D scanners, infrared, and other technologies that makes obtaining 3D data relatively much easier. 3D face recognition is still faced with several challenges. One of these is the lack of an available benchmark dataset that could be used for experimentation and thus provide a clear and solid indication of the robustness of any recogni- tion system. This is very clear if we knew that till 2003 the number of persons in datasets used for 3D face recognition experimentation didn’t reach 100 [3]. In addition, very few papers deals with pose and expression variation [3], [4], [5]. Several other challenging could be listed regarding the progress and development of 3D face recognition systems such as: Feature points allocation (this is still a debatable topic) that is also sensitive to the quality of data. Sampling density of the facial surface, and accuracy of the depth (e.g. no clear answer how much dense should a facial surface be? to accurately represent an individual face) [1]. No standard testing protocol is available to compare between different Face Recognition systems. Age factors, the size of the database, and efficiency of used algorithms are also considered as major challenges [3]. The rest of the paper is organized as follows: in the following section we will briefly review methodologies deployed in 3D Face Recognition systems. The next section, we will discuss our 3D image processing technique and our work on characterizing and allocating certain facial features automatically such as the tip of the nose, symmetry profile and cross-sectional profiles in the central region of the face. In addition, we will discuss our matching algorithm and the results, draw conclusion upon that and suggest future work for further improvement. II. PREVIOUS WORK Face recognition may be considered as a template match- ing with a high dimensionality. Dimensional reduction tech- nique is often used to reduce dimensionality in order to reduce computation cost. Kirby used Principle Component Analysis (PCA) in addressing the problem [6]. Among many various algorithm PCA [7] has become a corner stone in 2D face recognition system. However 2D Face Recognition systems are unable to overcome the problems mentioned earlier. Several approaches are used in the literature for 3D Face Recognition. Some of these are based on the segmentation of the face into meaningful points, lines and regions. Others are considered as model based approaches using information about texture, edges, and colors. Some techniques are con- sidered as a profile-based techniques where multiple profiles comparisons are carried out, by which a set of profiles are compared against each other, such profiles might be symmetry ones, transverse, vertical or even cross-sectional [8]–[10]. Among the existing approaches for addressing 3D face recognition systems is the use of extended Gaussian Image 2009 International Conference on CyberWorlds 978-0-7695-3791-7/09 $26.00 © 2009 IEEE DOI 10.1109/CW.2009.48 246