Robust Face Recognition System Based on a Multi-Views Face Database

Biometry is currently a very active area of research spanning several sub-disciplines such as image processing, pattern recognition, and computer vision. The main goal of biometry is to build systems that can identify people from some observable characteristics such as their face, fingerprints, iris, etc. Facial recognition is the identification of humans by the unique characteristics of their faces. It has become a specialized area within the large field of computer vision. It has attracted a lot of attention because of its potential applications. Indeed, vision systems that automate face recognition process appear to be promising in various fields such as law enforcement applications, secure information systems, multimedia systems, and cognitive sciences. The interest into face recognition is mainly focused on the identification requirements for secure information systems, multimedia systems, and cognitive sciences. Interest is still on the rise, since face recognition is also seen as an important part of next-generation smart environments (Ekenel & Sankur, 2004). Different techniques can be used to track and process faces (Yang et al, 2001), e.g., neural networks approaches (Férand et al., 2001, Rowley et al., 1998), eigenfaces (Turk & Pentland, 1991), or Markov chains (Slimane et al., 1999). As the recent DARPA-sponsored vendor test showed, much of the face recognition research uses the public 2-D face databases as the input pattern (Phillips et al., 2003), with a recognition performance that is often sensitive to pose and lighting conditions. One way to override these limitations is to combine modalities: color, depth, 3-D facial surface, etc. (Tsalakanidou et al., 2003, Beumier & Acheroy, 2001, Hehser et al., 2003, Lu et al., 2004, Bowyer et al., 2002). Most 3-D acquisition systems use professional devices such as a traveling camera or a 3-D scanner (Hehser et al., 2003, Lu et al., 2004). Typically, these systems require that the subject remain immobile during several seconds in order to obtain a 3-D scan, and therefore may not be appropriate for some applications such as human expression categorization using movement estimation. Moreover, many applications in the field of human face recognition such as humancomputer interfaces, model-based video coding, and security control (Kobayashi, 2001, Yeh & Lee, 1999) need to be high-speed and real-time, for example, passing through customs O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg