A New Attempt to Fast Recognition Using 3D Eigenfaces
Chenghua Xu, Yu-Hsing Wang, Tieniu Tan, Long Quan · Asian Conference on Computer Vision · 2004
Face recognition is a very challenging issue and has attracted much attention over the past decades. This paper makes a new attempt to face recognition based on 3D point clouds by constructing 3D eigenfaces. First, a 3D mesh model is built to represent the face shape provided by the point cloud. Then, the principle component analysis (PCA) is used to construct the 3D eigenfaces, which describe each mesh model in a lower-dimensional space. Finally, the nearest neighbor classifier and K-nearest neighbor classifier are utilized for recognition. Experimental results on 3D_RMA, a likely largest 3D face database available currently, show that the proposed algorithm has promising performance with a low computational cost.