Microexpression recognition model based on non negative matrix decomposition in intelligent classroom
At present, intelligent systems with the function of automatic micro expression recognition are gradually applied in intelligent classrooms, but there is still a problem of low recognition rate. Therefore, based on the dual graph regularization, the research constructs a joint non negative matrix de...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266730532400019X |
| _version_ | 1797294262432301056 |
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| author | Mingwei Li Jianyuan Li |
| author_facet | Mingwei Li Jianyuan Li |
| author_sort | Mingwei Li |
| collection | DOAJ |
| description | At present, intelligent systems with the function of automatic micro expression recognition are gradually applied in intelligent classrooms, but there is still a problem of low recognition rate. Therefore, based on the dual graph regularization, the research constructs a joint non negative matrix decomposition algorithm model for micro expression recognition, and verifies its effectiveness. The experimental results showed that the research algorithm had the highest performance in both micro and macro expression (micro) databases, at 75.4 %. In algorithm comparison, the recognition rates of algorithm L and algorithm M in different databases were higher than those of group A and B algorithms. Among them, Algorithm 6 in Group A had the highest recognition rate in all three databases, with the highest being 57.4 %; Algorithm 12 in Group B had the highest recognition rate in Zhongke Microexpression 2 and micro and macro expression databases, with 61.1 % and 59.4 %; Algorithm 11 had the highest recognition rate in self generated micro expression databases, with 54.0 %. And algorithm L and algorithm M had a minimum of 58.5 % and a maximum of 75.4 %. In parameter sensitivity analysis, the recognition rates of parameters η, χ, and γ in all databases showed good recognition results within a certain range of values, but they would decrease when they exceeded a specific value. Overall, the algorithm model proposed in the study has high effectiveness in improving the recognition rate of micro expressions, which is significant for the micro expression recognition of students in practical intelligent classrooms. |
| first_indexed | 2024-03-07T21:27:41Z |
| format | Article |
| id | doaj.art-532b324f421f4dbbb7661738cb4ca594 |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-03-07T21:27:41Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-532b324f421f4dbbb7661738cb4ca5942024-02-27T04:20:17ZengElsevierIntelligent Systems with Applications2667-30532024-06-0122200343Microexpression recognition model based on non negative matrix decomposition in intelligent classroomMingwei Li0Jianyuan Li1School of Information Engineering, Tangshan Polytechnic College, Tangshan, 063299, China; Corresponding author.School of Management Engineering, Tangshan Polytechnic College, Tangshan, 063299, ChinaAt present, intelligent systems with the function of automatic micro expression recognition are gradually applied in intelligent classrooms, but there is still a problem of low recognition rate. Therefore, based on the dual graph regularization, the research constructs a joint non negative matrix decomposition algorithm model for micro expression recognition, and verifies its effectiveness. The experimental results showed that the research algorithm had the highest performance in both micro and macro expression (micro) databases, at 75.4 %. In algorithm comparison, the recognition rates of algorithm L and algorithm M in different databases were higher than those of group A and B algorithms. Among them, Algorithm 6 in Group A had the highest recognition rate in all three databases, with the highest being 57.4 %; Algorithm 12 in Group B had the highest recognition rate in Zhongke Microexpression 2 and micro and macro expression databases, with 61.1 % and 59.4 %; Algorithm 11 had the highest recognition rate in self generated micro expression databases, with 54.0 %. And algorithm L and algorithm M had a minimum of 58.5 % and a maximum of 75.4 %. In parameter sensitivity analysis, the recognition rates of parameters η, χ, and γ in all databases showed good recognition results within a certain range of values, but they would decrease when they exceeded a specific value. Overall, the algorithm model proposed in the study has high effectiveness in improving the recognition rate of micro expressions, which is significant for the micro expression recognition of students in practical intelligent classrooms.http://www.sciencedirect.com/science/article/pii/S266730532400019XIntelligent classroomNMFMicro expressionRegularization of dual graph |
| spellingShingle | Mingwei Li Jianyuan Li Microexpression recognition model based on non negative matrix decomposition in intelligent classroom Intelligent Systems with Applications Intelligent classroom NMF Micro expression Regularization of dual graph |
| title | Microexpression recognition model based on non negative matrix decomposition in intelligent classroom |
| title_full | Microexpression recognition model based on non negative matrix decomposition in intelligent classroom |
| title_fullStr | Microexpression recognition model based on non negative matrix decomposition in intelligent classroom |
| title_full_unstemmed | Microexpression recognition model based on non negative matrix decomposition in intelligent classroom |
| title_short | Microexpression recognition model based on non negative matrix decomposition in intelligent classroom |
| title_sort | microexpression recognition model based on non negative matrix decomposition in intelligent classroom |
| topic | Intelligent classroom NMF Micro expression Regularization of dual graph |
| url | http://www.sciencedirect.com/science/article/pii/S266730532400019X |
| work_keys_str_mv | AT mingweili microexpressionrecognitionmodelbasedonnonnegativematrixdecompositioninintelligentclassroom AT jianyuanli microexpressionrecognitionmodelbasedonnonnegativematrixdecompositioninintelligentclassroom |