TY - GEN
T1 - Evaluation of a 3D-aided pose invariant 2D face recognition system
AU - Xu, Xiang
AU - Le, Ha A.
AU - Dou, Pengfei
AU - Wu, Yuhang
AU - Kakadiaris, Ioannis A.
N1 - Funding Information:
This material is based upon work supported by the U.S. Department of Homeland Security under Grant Award Number 2015-ST-061-BSH001. This grant is awarded to the Borders, Trade, and Immigration (BTI) Institute: A DHS Center of Excellence led by the University of Houston, and includes support for the project “Image and Video Person Identification in an Operational Environment” awarded to the University of Houston. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - 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.
AB - 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.
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U2 - 10.1109/BTAS.2017.8272729
DO - 10.1109/BTAS.2017.8272729
M3 - Conference contribution
AN - SCOPUS:85045580330
T3 - IEEE International Joint Conference on Biometrics, IJCB 2017
SP - 446
EP - 455
BT - IEEE International Joint Conference on Biometrics, IJCB 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Joint Conference on Biometrics, IJCB 2017
Y2 - 1 October 2017 through 4 October 2017
ER -