3D Face Recognition Using Spatial Relations

Abstract

Identity recognition using 3D scans of the face has been recently proposed as an alternative or complementary solution to conventional 2D face recognition approaches based on still images or videos. In fact, face representations based on 3D data are expected to be more robust to pose changes and illumination variations than 2D images, thus allowing accurate face recognition in real-world applications with unconstrained acquisition. Based on these premises, in this chapter, the authors first introduce the general and main methodologies for 3D face recognition, shortly reviewing the related literature by distinguishing between global and local approaches. Then, the authors present and discuss two 3D face recognition approaches that are robust to facial expression variations and share the common idea of accounting for the spatial relations between local facial features. In the first approach, the face is partitioned into iso-geodesic stripes and spatial relations are computed by integral measures that capture the relative displacement between the sets of 3D points in each pair of stripes. In the second solution, the face is described by detecting keypoints in the depth map of the face and locally describing them. Then, facial curves on the surface are considered between each pair of keypoints, so as to capture the shape of the face along the curve as well as the relational information between keypoints. Future research directions and conclusions are drawn at the end of the chapter.