Paper Info
Title
A fast and robust 3D face recognition approach based on deeply learned face representation.
Abstract
With the superiority of three-dimensional (3D) scanning data, e.g., illumination invariance and pose robustness, 3D face recognition theoretically has the potential to achieve better results than two-dimensional (2D) face recognition. However, traditional 3D face recognition techniques suffer from high computational costs. This paper proposes a fast and robust 3D face recognition approach with three component technologies: a fast 3D scan preprocessing, multiple data augmentation, and a deep learning technique based on facial component patches. First, unlike the majority of the existing approaches, which require accurate facial registration, the proposed approach uses only three facial landmarks. Second, the specifical deep network with an improved supervision is designed to extract complementary features from four overlapping facial component patches. Finally, a data augmentation technique and three self-collected 3D face datasets are used to enlarge the scale of the training data. The proposed approach outperforms the state-of-the-art algorithms on four public 3D face benchmarks, i.e., 100%, 99.75%, 99.88%, and 99.07% rank-1 IRs with the standard test protocol on the FRGC v2.0, Bosphorus, BU-3DFE, and 3D-TEC datasets, respectively. Further, it requires only 0.84 seconds to identify a probe from a gallery with 466 faces.
Year
DOI
Venue
2019
10.1016/j.neucom.2019.07.047
Neurocomputing
Keywords
Field
DocType
3D face recognition,Deep learning,Face preprocessing,Multiple data augmentation
Training set,Facial recognition system,Multiple data,Pattern recognition,Invariant (physics),Test protocol,Robustness (computer science),Preprocessor,Artificial intelligence,Deep learning,Mathematics
Journal
Volume
ISSN
Citations 
363
0925-2312
2
PageRank 
References 
Authors
0.36
0
5
Name
Order
Citations
PageRank
Ying Cai1101.85
Yinjie Lei217014.66
Menglong Yang351.43
Zhisheng You441752.22
Shiguang Shan56322283.75