FERLrTc: 2D+3D facial expression recognition via low-rank tensor completion
FERLrTc: 2D+3D facial expression recognition via low-rank tensor completion
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FERLrTC：通过低秩张量完成进行 2D 3D 面部表情识别
DOI：
10.1016/j.sigpro.2019.03.015
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发表时间：
2019-08
影响因子：
4.4
通讯作者：
Wan Jun
中科院分区：
文献类型：
--
作者：
Fu Yunfang;Ruan Qiuqi;Luo Ziyan;Jin Yi;An Gaoyun;Wan Jun
本文首先构建了一个四维张量模型，从多模态数据（二维和三维人脸数据）中挖掘有效的结构信息和相关性。由于生成的4D张量的维度很高，因此需要张量降维技术。由于现实世界中许多高阶数据往往驻留在低维子空间中，Tucker分解作为一种强大的技术被用来捕获多线性低秩结构，并从生成的4D张量数据中提取有用的信息。我们的目标是使用Tucker分解来获得一组具有较小尺寸和因子矩阵的核心张量，并将其投影到4D张量数据中进行分类预测。为了表征所涉及的相似性的4D张量，低秩和稀疏表示的因子矩阵的低秩结构和稀疏的核心张量的Tucker分解的生成的4D张量。嵌入了张量完成（TC）框架，以恢复4D张量建模过程中丢失的信息。为此，提出了一种新的基于低秩张量完备的二维+三维人脸表情识别张量降维方法（FERLrTC），通过降秩策略，以优化最小化的方式求解因子矩阵.在BU-3DFE和Bosphorus数据库和合成数据上进行了数值实验，以说明所提出的方法的有效性。
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.
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DOI：
10.1109/icpr.2010.907
发表时间：
2010-08
期刊：
2010 20th International Conference on Pattern Recognition
影响因子：
--
作者：
Xi Zhao;Di Huang;E. Dellandréa;Liming Chen
通讯作者：
Xi Zhao;Di Huang;E. Dellandréa;Liming Chen
基于克罗内克基表示的张量稀疏性及其在张量恢复中的应用
DOI：
10.1109/tpami.2017.2734888
发表时间：
2018
影响因子：
23.6
作者：
Xie Qi;Zhao Qian;Meng Deyu;Xu Zongben
通讯作者：
Xu Zongben
DOI：
10.1109/tencon.2013.6718813
发表时间：
2013-10
期刊：
2013 IEEE International Conference of IEEE Region 10 (TENCON 2013)
影响因子：
--
作者：
Xiaoli Li;Q. Ruan;Gaoyun An;Yi Jin
通讯作者：
Xiaoli Li;Q. Ruan;Gaoyun An;Yi Jin
DOI：
--
发表时间：
2012-11
期刊：
Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012)
影响因子：
--
作者：
Huibin Li;Liming Chen;Di Huang;Yunhong Wang;J. Morvan
通讯作者：
Huibin Li;Liming Chen;Di Huang;Yunhong Wang;J. Morvan
影响因子：
5.4
作者：
Haardt, Martin;Roemer, Florian;Del Galdo, Giovanni
通讯作者：
Del Galdo, Giovanni