Evaluation of a 3D-aided pose invariant 2D face recognition system

Abstract

A few well-developed face recognition pipelines have been reported in recent years. Most of the face-related work focuses on a specific module or demonstrates a research idea. In this paper, we present a pose-invariant 3D-aided 2D face recognition system (3D2D-PIFR) that is robust to pose variations as large as 90° by leveraging deep learning technology. We describe the architecture and the interface of 3D2D-PIFR, and introduce each module in detail. Experiments are conducted on the UHDB31 and IJB-A, demonstrating that 3D2D-PIFR outperforms existing 2D face recognition systems such as VGG-Face, FaceNet, and a commercial off-the-shelf software (COTS) by at least 9% on UHDB31 and 3% on IJB-A dataset on average. 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.