Geodesic Distance on Riemannian Manifold using Jacobi Iterations in 3D Face Recognition System

Abstract

In this paper, we present an automatic application of 3D face recognition system using geodesic distance in Riemannian geometry. We consider, in this approach, the three dimensional face images as residing in Riemannian manifold and we compute the geodesic distance using the Jacobi iterations as a solution of the Eikonal equation. The problem of solving the Eikonal equation, unstructured simplified meshes of 3D face surface, such as tetrahedral and triangles are important for accurately modeling material interfaces and curved domains, which are approximations to curved surfaces in R<sup>3</sup>. In the classifying steps, we use: Neural Networks (NN), K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). To test this method and evaluate its performance, a simulation series of experiments were performed on 3D Shape REtrieval Contest 2008 database (SHREC2008).<strong></strong>

(Retracted) Face recognition technology in classroom environment based on ResNet neural network

“…The results show that, compared with Euclidean distance (ED), using graphic drawing (GD) to extract image features is computationally more effective. 3 The development of computer technology has led to the development of face recognition technology. Today, with the help of computer and network technology, face recognition technology has been successfully applied in many fields.…”

“…conducted a series of experiments using a 2D facial image database (ORL and Yale). The results show that, compared with Euclidean distance (ED), using graphic drawing (GD) to extract image features is computationally more effective 3 . The development of computer technology has led to the development of face recognition technology.…”