Automatic landmark detection and 3D Face data extraction

This paper contributes to 3D facial synthesis by presenting a novel method for parameterization using Landmark Point detection. The approach presented aims at improving facial recognition even in varying facial expressions, and missing data in 3D facial models. As such, the prime objective was to develop an automatically embedded process that can detect any frontal face in 3D face recognition systems, with face segmentation and surface curvature information. Using the hybrid interpolation method, experiments on facial landmarks were performed on 4950 images from Face Recognition Grand Challenge database (FRGC). Distinctive facial landmarks from the nose-tips, Limits mouth and two eye corners formed the statistical inputs for Iterative Closest Point (ICP) in the Point Distribution Model (PDM). Performance or landmark localization is reported by using percentage deviation from the mean 3D profile. Localization results and estimated data on landmark locations demonstrate that the method confirms its effectiveness for proposed application.