A Survey of 3D Face Recognition Methods Alize Scheenstra 1 , Arnout Ruifrok 2 , and Remco C. Veltkamp 1 1 Utrecht University, Institute of Information and Computing Sciences, Padualaan 14, 3584 CH Utrecht, The Netherlands alize.scheenstra@tiscali.nl,remco.veltkamp@cs.uu.nl 2 Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB Den Haag, The Netherlands, arnout@holmes.nl Abstract. Many researches in face recognition have been dealing with the challenge of the great variability in head pose, lighting intensity and direction,facial expression, and aging. The main purpose of this overview is to describe the recent 3D face recognition algorithms. The last few years more and more 2D face recognition algorithms are improved and tested on less than perfect images. However, 3D models hold more in- formation of the face, like surface information, that can be used for face recognition or subject discrimination. Another major advantage is that 3D face recognition is pose invariant. A disadvantage of most presented 3D face recognition methods is that they still treat the human face as a rigid object. This means that the methods aren’t capable of handling fa- cial expressions. Although 2D face recognition still seems to outperform the 3D face recognition methods, it is expected that this will change in the near future. 1 Introduction One of the earliest face recognition methods was presented in 1966 by Bledsoe [1]. In one of his papers [2], Bledsoe described the difficulties of the face recognition problem: ”This recognition problem is made difficult by the great variability in head rotation and tilt, lighting intensity and angle, facial expression, aging, etc. Some other attempts at facial recognition by machine have allowed for little or no variability in these quantities. Yet the method of correlation (or pattern matching) of unprocessed optical data, which is often used by some researchers, is certain to fail in cases where the variability is great. In particular, the correlation is very low between two pictures of the same person with two different head rotations.” Since that time many researches have been dealing with this subject and have been trying to find an optimal face recognition method. The main purpose of this overview is to describe the recent face recognition algorithms on still im- ages. Previous face recognition surveys were presented by Samal and Iyengar [3],