An efficient 3D face recognition approach using Frenet feature of iso-geodesic curves

Abstract

Extracting efficient features from the large volume of 3D facial data directly is extremely difficult in 3D face recognition (3D-FR) with the latest methods, which mostly require heavy computations and manual processing steps. This paper presents a computationally efficient 3D-FR system based on a novel Frenet frame-based feature that is derived from the 3D facial iso-geodesic curves. In terms of the evaluation of the proposed method, we conducted a number of experiments on the CASIA 3D face database, and a superior recognition performance has been achieved. The performance evaluation suggests that the pose invariance attribute of the features relieves the need of an expensive 3D face registration in the face preprocessing procedure, where we take less time to process conversely. Our experiments further demonstrate that the proposed method not only achieves competitive recognition performance when compared with some existing techniques for 3D-FR, but also is computationally efficient.