3D Face Recognition with Sparse Spherical Representations

TL;DR

This paper introduces a novel 3D face recognition method using sparse spherical representations that effectively captures facial features and outperforms existing depth image-based solutions.

Contribution

The paper proposes a fully automated registration process and a sparse spherical approximation technique for 3D face recognition, enhancing discriminant feature preservation.

Findings

Outperforms classical state-of-the-art solutions on FRGC v.1.0 dataset

Uses sparse spherical functions to efficiently represent facial features

Achieves high recognition accuracy with reduced data dimensionality

Abstract

This paper addresses the problem of 3D face recognition using simultaneous sparse approximations on the sphere. The 3D face point clouds are first aligned with a novel and fully automated registration process. They are then represented as signals on the 2D sphere in order to preserve depth and geometry information. Next, we implement a dimensionality reduction process with simultaneous sparse approximations and subspace projection. It permits to represent each 3D face by only a few spherical functions that are able to capture the salient facial characteristics, and hence to preserve the discriminant facial information. We eventually perform recognition by effective matching in the reduced space, where Linear Discriminant Analysis can be further activated for improved recognition performance. The 3D face recognition algorithm is evaluated on the FRGC v.1.0 data set, where it is shown to outperform classical state-of-the-art solutions that work with depth images.