TY - GEN
T1 - High Quality Facial Data Synthesis and Fusion for 3D Low-quality Face Recognition
AU - Lin, Shisong
AU - Jiang, Changyuan
AU - Liu, Feng
AU - Shen, Linlin
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/4
Y1 - 2021/8/4
N2 - 3D face recognition (FR) is a popular topic in computer vision, since 3D face data is invariant to pose and illumination condition changes which easily affect the performance of 2D FR. Though many 3D solutions have achieved impressive performances on public high-quality 3D face databases, few works concentrate on low-quality 3D FR. As the quality of 3D face acquired by widely used low-cost RGB-D sensors is really low, more robust methods are required to achieve satisfying performance on these 3D face data. To address this issue, we propose a novel two-stage pipeline to improve the performance of 3D FR. In the first stage, we utilize pix2pix network to restore the quality of low-quality face. In the second stage, we launch a multi-quality fusion network (MQFNet) to fuse the features from different qualities and enhance FR performance. Our proposed network achieves the state-of-The-Art performance on the Lock3DFace database. Furthermore, extensive controlled experiments are conducted to demonstrate the effectiveness of each model of our network.
AB - 3D face recognition (FR) is a popular topic in computer vision, since 3D face data is invariant to pose and illumination condition changes which easily affect the performance of 2D FR. Though many 3D solutions have achieved impressive performances on public high-quality 3D face databases, few works concentrate on low-quality 3D FR. As the quality of 3D face acquired by widely used low-cost RGB-D sensors is really low, more robust methods are required to achieve satisfying performance on these 3D face data. To address this issue, we propose a novel two-stage pipeline to improve the performance of 3D FR. In the first stage, we utilize pix2pix network to restore the quality of low-quality face. In the second stage, we launch a multi-quality fusion network (MQFNet) to fuse the features from different qualities and enhance FR performance. Our proposed network achieves the state-of-The-Art performance on the Lock3DFace database. Furthermore, extensive controlled experiments are conducted to demonstrate the effectiveness of each model of our network.
UR - http://www.scopus.com/inward/record.url?scp=85113333568&partnerID=8YFLogxK
U2 - 10.1109/IJCB52358.2021.9484339
DO - 10.1109/IJCB52358.2021.9484339
M3 - Conference contribution
AN - SCOPUS:85113333568
T3 - 2021 IEEE International Joint Conference on Biometrics, IJCB 2021
BT - 2021 IEEE International Joint Conference on Biometrics, IJCB 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Joint Conference on Biometrics, IJCB 2021
Y2 - 4 August 2021 through 7 August 2021
ER -