Face Recognition using 3D Directional Corner Points

In this paper, we present a novel face recognition approach using 3D directional corner points (3D DCPs). Traditionally, points and meshes are applied to represent and match 3D shapes. Here we represent 3D surfaces by 3D DCPs derived from ridge and valley curves. Then we develop a 3D DCP matching method to compute the similarity of two different 3D surfaces. This representation, along with the similarity metric can effectively integrate structural and spatial information on 3D surfaces. The added information can provide more and better discriminative power for object recognition. It strengthens and improves the matching process of similar 3D objects such as faces. To evaluate the performance of our method for 3D face recognition, we have performed experiments on Face Recognition Grand Challenge v2.0 database (FRGC v2.0) and resulted in a rank-one recognition rate of 97.1%. This study demonstrates that 3D DCPs provides a new solution for 3D face recognition, which may also find its application in general 3D object representation and recognition.