3D Face Mesh Modeling for 3D Face Recognition

Abstract

Face recognition has rapidly emerged as an important area of research within many scientific and engineering disciplines. It has attracted research institutes, commercial industries, and numerous government agencies. This fact is evident by the existence of large number of face recognition conferences such as the International Conference on Automatic Face and Gesture and the Biometric Consortium conference. Special issues of well known journals, are being dedicated to face modeling and recognition, such as the journal of Computer Vision and Image Understanding (CVIU), and the systematic empirical evaluations of face recognition techniques including the FERET (Phillips et al., 2000), XM2VTS (Messer et al., 1999), FRVT 2000 (Blackburn et al., 2000), FRVT 2002 (Phillips et al., 2002), and FRVT 2006, which evolved substantially in the last few years. There are few reasons for this trend; first the demands for machine automations, securities, and law enforcements have created a wide range of commercial applications. The second is the availability of feasible technologies developed by researchers in the areas of image processing, pattern recognition, neural network, computer vision, and computer graphics. Another reason for this growing interest is to help us better understand ourselves through the fields of psychology and cognitive science which targeted the perception of faces in the brain. Because our natural face recognition abilities are very powerful, the study of the brain system could offer important guidance in the development of automatic face recognition. Research with animals has shown that these capabilities are not unique to humans. Sheep, for example, are known to have a remarkable memory for faces (Kendrick et al., 2000). In addition, we constantly use our faces while interacting with each others in a conversation. Face gesturing helps us understand what is being said. Facial expression is an important cue in understanding a person’s emotional state. In sign languages, faces also convey meanings that are essential part of the language. A wealth of 2D image-based algorithms has been published in the last few decades (Zhaho et al., 2003). Due to the numerous limitations of 2D approaches, 3D range image-based algorithms are born. Generally, 3D facial range image or data is rich, yet making full use of its high resolution for face recognition is very challenging. It is difficult to extract