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FERLrTc: 2D+3D facial expression recognition via low-rank tensor completion

文献类型：期刊论文

作者Fu, Yunfang1,2,3; Ruan, Qiuqi1,3; Luo, Ziyan4; Jin, Yi1,3; An, Gaoyun1,3; Wan, Jun5
刊名SIGNAL PROCESSING
出版日期2019-08-01
卷号161页码:74-88
关键词Tensor Tucker decomposition 2D+3D Facial expression recognition Tensor low-rank representation Tensor completion Multi-modality
ISSN号0165-1684
DOI10.1016/j.sigpro.2019.03.015
通讯作者Fu, Yunfang(fu_yunfang@126.com)
英文摘要In this paper, a 4D tensor model is firstly constructed to explore efficient structural information and correlations from multi-modal data (both 2D and 3D face data). As the dimensionality of the generated 4D tensor is high, a tensor dimensionality reduction technique is in need. Since many real-world high-order data often reside in a low dimensional subspace, Tucker decomposition as a powerful technique is utilized to capture multilinear low-rank structure and to extract useful information from the generated 4D tensor data. Our goal is to use Tucker decomposition to obtain a set of core tensors with smaller sizes and factor matrices which are projected into the 4D tensor data for classification prediction. To characterize the involved similarities of the 4D tensor, the low-rank and sparse representation is built in terms of the low-rank structure of factor matrices and the sparsity of the core tensor in the Tucker decomposition of the generated 4D tensor. A tensor completion (TC) framework is embedded to recover the missing information in the 4D tensor modeling process. Thus, a novel tensor dimensionality reduction approach for 2D+3D facial expression recognition via low-rank tensor completion (FERLrTC) is proposed to solve the factor matrices in a majorization-minimization manner by using a rank reduction strategy. Numerical experiments are conducted with a full implementation on the BU-3DFE and Bosphorus databases and synthetic data to illustrate the effectiveness of the proposed approach. (C) 2019 Elsevier B.V. All rights reserved.
WOS关键词FEATURE-SELECTION ; 3-D FACE ; 3D ; DECOMPOSITIONS
资助项目National Natural Science Foundation of China[61471032] ; National Natural Science Foundation of China[11771038] ; National Natural Science Foundation of China[11431002] ; National Natural Science Foundation of China[61403024] ; National Natural Science Foundation of China[61472030] ; National Natural Science Foundation of China[61772067] ; National Natural Science Foundation of China[61502491] ; Program for Innovative Research Team in University of Ministry of Education of China[IRT201206] ; Program for New Century Excellent Talents in University[NCET-12-0768] ; Fundamental Research Funds for the Central Universities[2017JBZ108]
WOS研究方向Engineering
语种英语
WOS记录号WOS:000466258200007
出版者ELSEVIER SCIENCE BV
资助机构National Natural Science Foundation of China ; Program for Innovative Research Team in University of Ministry of Education of China ; Program for New Century Excellent Talents in University ; Fundamental Research Funds for the Central Universities
源URL[http://ir.ia.ac.cn/handle/173211/24597]  
专题自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
通讯作者Fu, Yunfang
作者单位1.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China
2.Shijiazhuang Univ, Sch Comp Sci & Engn, Shijiazhuang 050035, Hebei, Peoples R China
3.Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China
4.Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China
5.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Fu, Yunfang,Ruan, Qiuqi,Luo, Ziyan,et al. FERLrTc: 2D+3D facial expression recognition via low-rank tensor completion[J]. SIGNAL PROCESSING,2019,161:74-88.
APA Fu, Yunfang,Ruan, Qiuqi,Luo, Ziyan,Jin, Yi,An, Gaoyun,&Wan, Jun.(2019).FERLrTc: 2D+3D facial expression recognition via low-rank tensor completion.SIGNAL PROCESSING,161,74-88.
MLA Fu, Yunfang,et al."FERLrTc: 2D+3D facial expression recognition via low-rank tensor completion".SIGNAL PROCESSING 161(2019):74-88.

入库方式： OAI收割

来源：自动化研究所

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