Introduction Biometric systems for human recognition are an ongoing demand. Among all biometric technologies which are employed so far, face recognition is one of the most widely outspread biometrics. Its daily use by nearly everyone as the primary mean for recognizing other humans and its naturalness have turned face recognition into a well-accepted method. Furthermore, this image procurement is not considered as intrusive as the other mentioned alternatives. Nonetheless, in spite of the

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... s facial recognition systems which already exist, many of them have been unsuccessful in matching up to expectations. 2D facial recognition systems are constrained by limitations such as physical appearance changes, aging factor, pose and changes in lighting intensity. Recently, to overcome these challenges 3D facial recognition systems have been issued as the newly emerged biometric technique, showing a high level of accuracy and reliability, being more robust to face variation due to the different factors. A face-based biometric system consists of acquisition devices, preprocessing, feature extraction, data storage and a comparator. An acquisition device may be a 2D-, 3D-or an infra-red-camera that can record the facial information. The preprocessing can detect facial landmarks, align facial data and crop facial area. It can filter irrelevant information such as hair, background and reduce facial variation due to pose change. In 2D images, landmarks such as eye, eyebrow, mouths etc, can be reliably detected, in contrast, nose is the most important landmark in 3D face recognition. The 3D information (depth and texture maps) corresponding to the surface of the face may be acquired using different alternatives: A multi camera system (stereoscopy), range cameras or 3D laser and scanner devices. Different approaches have been presented from the 3D perspective. The first approach would correspond to all 3D approaches that require the same data format in the training and in the testing stage. The second philosophy would enclose all approaches that take advantage of the 3D data during the training stage but then use 2D data in the recognition stage. Approaches of the first category report better results t h a n o f t h e s e c o n d g r o u p ; h o w e v e r , t h e m a i n d r a w b a c k o f t h i s c a t e g o r y i s t h a t t h e acquisition conditions and elements of the test scenario should be well synchronized and controlled in order to acquire accurate 3D data. Thus, they are not suitable for surveillance applications or control access points where only one "normal" 2D texture image (from any view) acquired from a single camera is available. The second category encloses model-based approaches. Nevertheless, model-based face recognition approaches present the main drawback of a high computational burden required to fit the images to the 3D models. www.intechopen.com New Approaches to Characterization and Recognition of Faces 48 In this chapter, we study 3D face recognition where we provide a description of the most recent 3D based face recognition techniques and try to coarsely classify them into categories, as explained in the following subsequent sections. (Maurer et al., 2005) presented a multimodal algorithm that uses Iterative Closest Point (ICP) to extract distance map, which is the distance between mesh of reference and probe. This method includes, face finding, landmark finding, and template computation. They used weighted sum rule to fuse shape and texture scores. If 3D score is high, algorithm uses only shape for evaluation. In experimental tests by using 4007 faces in the FRGC v2 database, a verification rate of 87.0% was achieved at %0.1 false accept rate (FAR). performed face recognition with an annotated model that is non-rigidly registered to face meshes through a combination of ICP, simulated annealing and elastically adapted deformable model fitting. A limitation of this approach is the imposed constraints on the initial orientation of the face. Performance of 3D methods highly depends on registration performance, where ICP is commonly used. ICP registration performance is highly dependent on initial alignment and it performs solid registration. However, expression variations degrade registration success. To overcome this problem, (Faltemier et al., 2008) divided the face into different overlapping regions where each face region was registered independently. Distance between regions was used as a similarity measure and results were fused using modified Borda count. They achieved 97.2% rate on FRGC v2 database. Other approaches to discard the effect of expressions were also studied by dividing the face into separate parts and extracting features from each part in 2D and range images (Cook et al., 2006; McCool et al., 2008) . Iterative closest point