surfaces continuously in two parametric directions with the help of control points and knots. Details of NURBS and B-splines can be found in [74].

A large number of visualization tools are available for rendering and manipulating 3D data. Most are built using the Open Graphics Library (OpenGL) and can render point clouds, meshes, filled polygons with constant shading or texture mappings. Likewise, file formats are numerous, however, they all follow the same convention i.e. a header followed by the X, Y, Z coordinates of the point cloud and the list of polygons. The header usually contains information about the sensor, rendering parameters, the number of points and polygons and so on. The polygon list contains index numbers of points that form a polygon. A few examples of free visualization and manipulation tools are PCL, MeshLab [68], Plyview, Blender and Scanalyze. Proprietary softwares include Maya, Rhino and Poser. Online datasets of 3D faces are also on the increase and the most well-known of these are discussed in the following section.

8.3 3D Face Datasets

A range of 3D face datasets have been employed by the research community for 3D face recognition and verification evaluations. Examples with a large number (>1000) of images include

•The Face Recognition Grand Challenge v2 3D dataset (FRGC v2) [73].

•The Bosphorus 3D face dataset (Bosphorus) [13].

•The Texas 3D face recognition dataset (Texas3DFRD) [86].

•The Notre Dame 3D face dataset (ND-2006) [30].

•The Binghamton University 3D facial expression dataset(BU-3DFE) [93].

•The Chinese Academy of Sciences Institute of Automation 3D dataset (CASIA) [16].

•The University of York 3D face dataset (York3DFD) [45].

Datasets are characterized by the number of 3D face images captured, the number of subjects imaged, the quality and resolution of the images and the variations in facial pose, expression, and occlusion. Table 8.1 outlines this characterization for the datasets mentioned above.

Datasets comprising several thousand images, gathered from several hundred subjects, allow researchers to observe relatively small differences in performance in a statistically significant way. Different datasets are more or less suitable for evaluating different types of face recognition scenario. For example, a frontal view, neutral expression dataset is suitable for ‘verification-of-cooperative-subject’ evaluations but a dataset with pose variations, expression variations and occlusion by head accessories (such as hat and spectacles) is necessary for evaluating a typical ‘identification-at-a-distance’ scenario for non-cooperating subjects who may even be unaware that they are being imaged. This type of dataset is more challenging to collect as all common combinations of variations need to be covered, leading to a much larger number of captures per subject.

Fig. 8.3 Top row: 2D images reprinted from the FRGC v2 dataset [73]. Second row: a rendering of the corresponding 3D data, after conversion from ABS to OBJ format

Often the texture channel is used to mark up fiducial points on the face, such as eye corners, nose-tip and mouth corners, and these are then mapped onto the range data using the known, implicit registration to give 3D landmarks. In this case, and in other cases where channel registration is important, these 3D scans must not be used.

A key point in the proper evaluation of any supervised training system is that the differences between the training and testing 3D face scans must be comparable to what is expected when test data is gathered in a live operational recognition scenario. In dataset development, capturing consecutive scans of the same person leads to train/test data that is too similar for proper evaluation, hence the different collection periods of the FRGC v2 dataset, which is divided into three sections, Spring-2003, Fall-2003 and Spring-2004, named after their periods of collection. The Spring2003 set is regarded by Phillips et al. [73] as a training set. It consists of 943 3D face images, with subjects in a cooperating, near frontal pose and a neutral facial expression. The majority of the images in this subset are at a depth such that the neck and shoulder areas are also imaged. In contrast, a small number of the images contain close-up shots, where only the facial area is imaged. Thus the resolution of imaged facial features is higher in these scans. Also, within this training subset, ambient lighting is controlled.

In the Fall-2003 and Spring-2004 datasets, ambient lighting is uncontrolled, and facial expressions are varied. Expressions include any of anger, happiness, sadness, surprise, disgust and ‘puffy cheeks’, although many subjects are not scanned with the full set of these expressions. Additionally, there is less depth variation than in the Spring-2003 subset and all of the images are close ups with little shoulder area captured. Again pose can be regarded as frontal and cooperative, but there are some mild pose variations. In these data subsets there are 4007 two-channel scans of 466 subjects (1–22 scans per subject) and Phillips et al. [73] regard these as validation datasets.