Convolutional Channel Attentional Facial Expression Recognition Network and Its Application in Human–Computer Interaction
Currently, the use of robots has altered the way people live and their lifestyles. To realize a human-computer interaction system based on robots’ comprehension of human emotions, this study chooses facial expressions as the research object and constructs a facial expression recognition m...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10319416/ |
| _version_ | 1797473525051686912 |
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| author | Jing Pu Xinxin Nie |
| author_facet | Jing Pu Xinxin Nie |
| author_sort | Jing Pu |
| collection | DOAJ |
| description | Currently, the use of robots has altered the way people live and their lifestyles. To realize a human-computer interaction system based on robots’ comprehension of human emotions, this study chooses facial expressions as the research object and constructs a facial expression recognition model based on convolutional neural networks and channel attention. To deploy the recognition model to portable devices, a depth-separable convolution filter pruning algorithm based on principal component analysis is constructed. This model applies principal component analysis to reduce the dimensionality of similar filter matrices obtained by calculating geometric medians to prevent gradient explosion. The proposed algorithm for facial expression recognition in this study achieves a 99% recognition accuracy with an average of 80.39%, while using the least number of parameters among the compared algorithms. The verification experiment results of lightweight network show that when the model depth is 56 and the pruning rate is 40%, the correct rate of facial expression recognition of the network model based on the pruning strategy proposed in the study is 93.24%. When dimension is 0.85, the accuracy of model classification is the highest. The algorithm presented in this study exhibits excellent performance when recognizing facial expressions, demonstrating notable robustness and efficiency. The pruning strategy proposed in this study has a good model acceleration effect. It can not only reduce the memory occupied by about 41% of parameters, but also improve the classification accuracy, running time and calculation cost after pruning to a certain extent. The study applied deep separable convolution and PCA techniques to improve and reduce the dimensionality of the convolutional layer in the ResNet network structure, and improved the comparable filter matrix generated during the filter pruning process. |
| first_indexed | 2024-03-09T20:15:42Z |
| format | Article |
| id | doaj.art-9e21d8fc18da40c8b061c4b2751d44bb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-09T20:15:42Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9e21d8fc18da40c8b061c4b2751d44bb2023-11-24T00:01:24ZengIEEEIEEE Access2169-35362023-01-011112941212942410.1109/ACCESS.2023.333338110319416Convolutional Channel Attentional Facial Expression Recognition Network and Its Application in Human–Computer InteractionJing Pu0https://orcid.org/0009-0007-5791-6713Xinxin Nie1School of Arts and Media, Sichuan Agricultural University, Yaan, ChinaCollege of Literature and Media, Chengdu Jincheng College, Chengdu, ChinaCurrently, the use of robots has altered the way people live and their lifestyles. To realize a human-computer interaction system based on robots’ comprehension of human emotions, this study chooses facial expressions as the research object and constructs a facial expression recognition model based on convolutional neural networks and channel attention. To deploy the recognition model to portable devices, a depth-separable convolution filter pruning algorithm based on principal component analysis is constructed. This model applies principal component analysis to reduce the dimensionality of similar filter matrices obtained by calculating geometric medians to prevent gradient explosion. The proposed algorithm for facial expression recognition in this study achieves a 99% recognition accuracy with an average of 80.39%, while using the least number of parameters among the compared algorithms. The verification experiment results of lightweight network show that when the model depth is 56 and the pruning rate is 40%, the correct rate of facial expression recognition of the network model based on the pruning strategy proposed in the study is 93.24%. When dimension is 0.85, the accuracy of model classification is the highest. The algorithm presented in this study exhibits excellent performance when recognizing facial expressions, demonstrating notable robustness and efficiency. The pruning strategy proposed in this study has a good model acceleration effect. It can not only reduce the memory occupied by about 41% of parameters, but also improve the classification accuracy, running time and calculation cost after pruning to a certain extent. The study applied deep separable convolution and PCA techniques to improve and reduce the dimensionality of the convolutional layer in the ResNet network structure, and improved the comparable filter matrix generated during the filter pruning process.https://ieeexplore.ieee.org/document/10319416/Channel attentiondepth-separable convolutionfacial expression recognitionhuman–computer interactionfilter pruningprincipal component analysis |
| spellingShingle | Jing Pu Xinxin Nie Convolutional Channel Attentional Facial Expression Recognition Network and Its Application in Human–Computer Interaction IEEE Access Channel attention depth-separable convolution facial expression recognition human–computer interaction filter pruning principal component analysis |
| title | Convolutional Channel Attentional Facial Expression Recognition Network and Its Application in Human–Computer Interaction |
| title_full | Convolutional Channel Attentional Facial Expression Recognition Network and Its Application in Human–Computer Interaction |
| title_fullStr | Convolutional Channel Attentional Facial Expression Recognition Network and Its Application in Human–Computer Interaction |
| title_full_unstemmed | Convolutional Channel Attentional Facial Expression Recognition Network and Its Application in Human–Computer Interaction |
| title_short | Convolutional Channel Attentional Facial Expression Recognition Network and Its Application in Human–Computer Interaction |
| title_sort | convolutional channel attentional facial expression recognition network and its application in human x2013 computer interaction |
| topic | Channel attention depth-separable convolution facial expression recognition human–computer interaction filter pruning principal component analysis |
| url | https://ieeexplore.ieee.org/document/10319416/ |
| work_keys_str_mv | AT jingpu convolutionalchannelattentionalfacialexpressionrecognitionnetworkanditsapplicationinhumanx2013computerinteraction AT xinxinnie convolutionalchannelattentionalfacialexpressionrecognitionnetworkanditsapplicationinhumanx2013computerinteraction |