Sparse and Low-rank Tucker Decomposition with Its Application to 2D+3D Facial Expression Recognition

Fu, Youhua; Ruan, Qiuqi; Jiang, Yajie

doi:10.1109/icsp48669.2020.9321082

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…To alleviate this issue, a more natural way to describe 3D facial expression data is using tensors, which not only maintains the spacial structure but also admits sparse representation when appropriate tensor decomposition is chosen, by employing tools from tensor analysis. At present, the existing methods using tensors to describe 3D facial expression data are mostly based on tensor decomposition [13][14][15][16][17]20,21], which have opened up a new technology direction and made some progress. However, the local structure (geometric information) of 3D tensor samples in these methods are not maintained in the low-dimensional tensors space during the dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

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

Ruan

Luo

et al. 2019

Signal Processing

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Section: Introductionmentioning

confidence: 99%

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

Ruan

Luo

et al. 2019

Signal Processing

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“…In recent years, there are several tensor decompositionbased methods [25][26][27][28] which have been proposed for FER. For instance, in [26], a method based on low-rank tensor completion is proposed and applied into the 2D+3D FER.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal tucker decomposition using factor priors for 2D+3D facial expression recognition

Ruan

Luo

et al. 2021

IET Biometrics

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In this article, an effective approach is proposed to recognise the 2D+3D facial expression automatically based on orthogonal Tucker decomposition using factor priors (OTDFPFER). As a powerful technique, Tucker decomposition on the basis of the low rank approximation is often used to extract the useful information from the constructed 4D tensor composed of 3D face scans and 2D images aiming to maintain correlations and their structural information. Finding a set of projected factor matrices is our ultimate goal. During the 4D tensor modelling process, high similarities among samples will emerge because of the information missed partially. Based on the tensor orthogonal Tucker decomposition, the involved core tensor with the structured sparsity, and a graph regularisation term via the graph Laplacian matrix together with the fourth factor matrix are employed for better characterisation of the generated similarities and for keeping the consistency of low dimensional space. To recover the missing information, a framework for tensor completion (TC) will be embedded naturally. Finally, an alternating direction method coupled with the majorisation-minimisation scheme is designed to solve the resulting tensor completion problem. The numerical experiments are conducted on the Bosphorus and the BU-3DFE databases with promising recognition accuracies.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Low Rank Tucker Decomposition for 2D+3D Facial Expression Recognition

Deng

Zhang

et al. 2022

Procedia Computer Science

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Sparse and Low-rank Tucker Decomposition with Its Application to 2D+3D Facial Expression Recognition

Cited by 4 publications

References 41 publications

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

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

Orthogonal tucker decomposition using factor priors for 2D+3D facial expression recognition

Low Rank Tucker Decomposition for 2D+3D Facial Expression Recognition

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