3D Face Reconstruction from a Single Image Using a Combined PCA-LPP Method

Abstract

In this paper, we proposed a combined PCA-LPP algorithm to improve 3D face reconstruction performance. Principal component analysis (PCA) is commonly used to compress images and extract features. One disadvantage of PCA is local feature loss. To address this, various studies have proposed combining a PCA-LPP-based algorithm with a locality preserving projection (LPP). However, the existing PCA-LPP method is unsuitable for 3D face reconstruction because it focuses on data classification and clustering. In the existing PCA-LPP, the adjacency graph, which primarily shows the connection relationships between data, is composed of the e-or k-nearest neighbor techniques. By contrast, in this study, complex and detailed parts, such as wrinkles around the eyes and mouth, can be reconstructed by composing the topology of the 3D face model as an adjacency graph and extracting local features from the connection relationship between the 3D model vertices. Experiments verified the effectiveness of the proposed method. When the proposed method was applied to the 3D face reconstruction evaluation set, a performance improvement of 10% to 20% was observed compared with the existing PCA-based method.