Pure Face Extraction from 3D Point Cloud Using Random Forest Skin Classification

Abstract

Using 3D information is expected to handle challenges in 2D face recognition and improve system performance. Extracting pure facial part in face point cloud is usually the first step in a 3D face recognition system, which was mainly operated by manual in most previous studies. In this paper we propose a fully automatic approach for pure face extraction from 3D point cloud. Considering that 3D face point cloud can often be sensed in combination with color information, we use random forest classifiers to classify skin points and non-skin points in 3D point clouds. Usually there will be a few holes in the obtained skin point cloud, which mainly correspond to eyes, mouth, moustache, etc. We propose an approach based on nearest neighbor search method to fulfill the holes. Experiments show that the proposed approach can extract pure faces with different sizes, poses and expressions under various illumination conditions.

This paper was supported by (1) National Natural Science Foundation of China under the Grant No. 61170116;61375010; (2) National Natural Science Foundation of China under the Grant No. 61005009; (3) Beijing Municipal Natural Science Foundation under the Grant No. 4102039; (4) Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality (PHR201108261). (5) Beijing Higher Education Young Elite Teacher Project under the Grant No. YETP0375. (6) Portions of the research in this paper use the CASIA-3D FaceV1 collected by the Chinese Academy of Sciences’ Institute of Automation (CASIA).