When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition

TL;DR

This paper introduces UR2D, a 3D-aided 2D face recognition system leveraging deep learning, which achieves high pose-invariance and outperforms existing methods on challenging datasets.

Contribution

The paper presents a novel pose-invariant 3D-aided 2D face recognition system (UR2D) that integrates deep learning and demonstrates superior performance on benchmark datasets.

Findings

UR2D outperforms existing 2D face recognition systems by at least 9% on UHDB31.

UR2D achieves 85% Rank-1 accuracy on IJB-A, setting a new state-of-the-art.

The system is robust to pose variations up to 90 degrees.

Abstract

Most of the face recognition works focus on specific modules or demonstrate a research idea. This paper presents a pose-invariant 3D-aided 2D face recognition system (UR2D) that is robust to pose variations as large as 90? by leveraging deep learning technology. The architecture and the interface of UR2D are described, and each module is introduced in detail. Extensive experiments are conducted on the UHDB31 and IJB-A, demonstrating that UR2D outperforms existing 2D face recognition systems such as VGG-Face, FaceNet, and a commercial off-the-shelf software (COTS) by at least 9% on the UHDB31 dataset and 3% on the IJB-A dataset on average in face identification tasks. UR2D also achieves state-of-the-art performance of 85% on the IJB-A dataset by comparing the Rank-1 accuracy score from template matching. It fills a gap by providing a 3D-aided 2D face recognition system that has compatible results with 2D face recognition systems using deep learning techniques.