Study of Two 3D Face Representation Algorithms Using Range Image and Curvature-Based Representations

Abstract

In this paper we present a comparative analysis of two algorithms for image representation with application to recognition of 3D face scans with the presence of facial expressions. We begin with processing of the input point cloud based on curvature analysis and range image representation to achieve a unique representation of the face features. Then, subspace projection using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) is performed. Finally classification with different classifiers will be performed over the 3D face scans dataset with 61 subject with 7 scans per subject (427 scans), namely two "frontal", one "look-up", one "look-down", one "smile", one "laugh", one "random expression". The experimental results show a high recognition rate for the chosen database. They demonstrate the effectiveness of the proposed 3D image representations and subspace projection for 3D face recognition.