Nose Tip Detection in 3D Face Image Based On Maximum Intensity Algorithm

Abstract

In order to increase the ability to track face movements with large head rotations, a 3D shape model is used in the system. In this paper, we present a robust nose tip detection method in 3D facial image that handling facial expression and hair occlusion. The 3D face smoothed by weighted median filter, the holes are filled by linear interpolation during the re-sampling phase and the 3D Gaussian filter is used to remove noise. Since the database, mesh contains unimportant parts like neck, shoulder, clothes and hair that can also change the overall appearance of a face. We propose a 3D mask to cut the face and crop useful part, which helps us to achieve sufficient accuracy for noise detection. Nose localization is one of the most significant tasks of any facial classification system, compared to other facial landmarks. We proceed to detect the point N from the front image following the assumption that the relevant point has the highest value of the Z axis. In this framework, the face model is determined from a frontal 3D face image. In this experiment, the performance rate is improved from 90.1% to 98.3%. As indicated by the experimental result, the proposed binary mask with maximum intensity method provides a significant improvement in performance of nose tip detection, also it success in different facial expression.