A Novel FNP-Pose Estimation for Three-Dimensional Face Recognition using DPCA Under Facial Expression

This paper presents an efficient 3D face recognition algorithm with facial expression. The proposed algorithm describes a novel FNP (fast nose points)-pose estimation which is the Triangle-based four point method to estimate the pose of three-dimensional (3-D) face, (i.e. the 3-D shape). Firstly, we find the specific points using Angle comparison Trace. Secondly, from this triangle we calculate weight point of triangle which is used for translation and compensate the rotation of 3D test facial image. Finally, Depth PCA is employed for 3D face recognition where it performs PCA on a 2D x-y axis and takes into account the depth information from 3D Vertices and Face entries. 2D texture information is mapped corresponding to each point at centre of vertices and segment 2-by-2 around this point. The proposed algorithm allows the use of fast iterative algorithm to compute the 3-D facial pose and 3D face recognition that best fits the data. The algorithm has been tested with 3D database and obtained results provide a high level of robustness and accurate recognition.