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A fast and robust 3D face recognition approach based on deeply learned face representation

文献类型：期刊论文

作者Cai, Ying2,5; Lei, Yinjie1; Yang, Menglong3,5; You, Zhisheng3,5; Shan, Shiguang4
刊名NEUROCOMPUTING
出版日期2019-10-21
卷号363页码:375-397
ISSN号0925-2312
关键词3D face recognition Deep learning Face preprocessing Multiple data augmentation
DOI10.1016/j.neucom.2019.07.047
英文摘要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. (C) 2019 Elsevier B.V. All rights reserved.
资助项目Chinese Universities Scientific Fund[2019NYB05] ; National Natural Science Foundation of China[61702350] ; National Natural Science Foundation of China[61402307] ; National Natural Science Foundation of China[61403265] ; National Natural Science Foundation of China[71774134] ; National Natural Science Foundation of China[71373216] ; Sichuan Science and Technology Program[18YYJC1287] ; Sichuan Science and Technology Program[2015SZ0226] ; Sichuan University[2018SCUH0042] ; National Key Research and Development Program of China[2016YFC0801100] ; National Key Scientific Instrument and Equipment Development Project of China[2013YQ49087903]
WOS研究方向Computer Science
语种英语
出版者ELSEVIER
WOS记录号WOS:000484005300034
源URL[http://119.78.100.204/handle/2XEOYT63/4711]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Yang, Menglong
作者单位1.Sichuan Univ, Sch Elect & Informat Engn, Chengdu, Sichuan, Peoples R China
2.Southwest Minzu Univ, Sch Elect & Informat Engn, Chengdu, Sichuan, Peoples R China
3.Sichuan Univ, Sch Aeronaut & Astronaut, Chengdu, Sichuan, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China
5.Wisesoft Software Co Ltd, Chengdu, Sichuan, Peoples R China
推荐引用方式
GB/T 7714		 Cai, Ying,Lei, Yinjie,Yang, Menglong,et al. A fast and robust 3D face recognition approach based on deeply learned face representation[J]. NEUROCOMPUTING,2019,363:375-397.
APA Cai, Ying,Lei, Yinjie,Yang, Menglong,You, Zhisheng,&Shan, Shiguang.(2019).A fast and robust 3D face recognition approach based on deeply learned face representation.NEUROCOMPUTING,363,375-397.
MLA Cai, Ying,et al."A fast and robust 3D face recognition approach based on deeply learned face representation".NEUROCOMPUTING 363(2019):375-397.

入库方式： OAI收割

来源：计算技术研究所

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