Orthogonal tucker decomposition using factor priors for 2D+3D facial expression recognition
Abstract 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 extrac...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-11-01
|
| Series: | IET Biometrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/bme2.12035 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832546738364219392 |
|---|---|
| author | Yunfang Fu Qiuqi Ruan Ziyan Luo Gaoyun An Yi Jin |
| author_facet | Yunfang Fu Qiuqi Ruan Ziyan Luo Gaoyun An Yi Jin |
| author_sort | Yunfang Fu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-75ba92604d5f440bb75af1c20d8bb85a |
| institution | Kabale University |
| issn | 2047-4938 2047-4946 |
| language | English |
| publishDate | 2021-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-75ba92604d5f440bb75af1c20d8bb85a2025-02-03T06:47:18ZengWileyIET Biometrics2047-49382047-49462021-11-0110666467810.1049/bme2.12035Orthogonal tucker decomposition using factor priors for 2D+3D facial expression recognitionYunfang Fu0Qiuqi Ruan1Ziyan Luo2Gaoyun An3Yi Jin4Institute of Information Science Beijing Jiaotong University Beijing ChinaInstitute of Information Science Beijing Jiaotong University Beijing ChinaDepartment of Mathematics Beijing Jiaotong University Beijing ChinaInstitute of Information Science Beijing Jiaotong University Beijing ChinaInstitute of Information Science Beijing Jiaotong University Beijing ChinaAbstract 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.https://doi.org/10.1049/bme2.12035emotion recognitionface recognitiongraph theorymatrix decompositiontensors |
| spellingShingle | Yunfang Fu Qiuqi Ruan Ziyan Luo Gaoyun An Yi Jin Orthogonal tucker decomposition using factor priors for 2D+3D facial expression recognition IET Biometrics emotion recognition face recognition graph theory matrix decomposition tensors |
| title | Orthogonal tucker decomposition using factor priors for 2D+3D facial expression recognition |
| title_full | Orthogonal tucker decomposition using factor priors for 2D+3D facial expression recognition |
| title_fullStr | Orthogonal tucker decomposition using factor priors for 2D+3D facial expression recognition |
| title_full_unstemmed | Orthogonal tucker decomposition using factor priors for 2D+3D facial expression recognition |
| title_short | Orthogonal tucker decomposition using factor priors for 2D+3D facial expression recognition |
| title_sort | orthogonal tucker decomposition using factor priors for 2d 3d facial expression recognition |
| topic | emotion recognition face recognition graph theory matrix decomposition tensors |
| url | https://doi.org/10.1049/bme2.12035 |
| work_keys_str_mv | AT yunfangfu orthogonaltuckerdecompositionusingfactorpriorsfor2d3dfacialexpressionrecognition AT qiuqiruan orthogonaltuckerdecompositionusingfactorpriorsfor2d3dfacialexpressionrecognition AT ziyanluo orthogonaltuckerdecompositionusingfactorpriorsfor2d3dfacialexpressionrecognition AT gaoyunan orthogonaltuckerdecompositionusingfactorpriorsfor2d3dfacialexpressionrecognition AT yijin orthogonaltuckerdecompositionusingfactorpriorsfor2d3dfacialexpressionrecognition |