CAGNet: A Network Combining Multiscale Feature Aggregation and Attention Mechanisms for Intelligent Facial Expression Recognition in Human-Robot Interaction
The development of Facial Expression Recognition (FER) technology has significantly enhanced the naturalness and intuitiveness of human-robot interaction. In the field of service robots, particularly in applications such as production assistance, caregiving, and daily service communication, efficien...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/12/3653 |
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| author | Dengpan Zhang Wenwen Ma Zhihao Shen Qingping Ma |
| author_facet | Dengpan Zhang Wenwen Ma Zhihao Shen Qingping Ma |
| author_sort | Dengpan Zhang |
| collection | DOAJ |
| description | The development of Facial Expression Recognition (FER) technology has significantly enhanced the naturalness and intuitiveness of human-robot interaction. In the field of service robots, particularly in applications such as production assistance, caregiving, and daily service communication, efficient FER capabilities are crucial. However, existing Convolutional Neural Network (CNN) models still have limitations in terms of feature representation and recognition accuracy for facial expressions. To address these challenges, we propose CAGNet, a novel network that combines multiscale feature aggregation and attention mechanisms. CAGNet employs a deep learning-based hierarchical convolutional architecture, enhancing the extraction of features at multiple scales through stacked convolutional layers. The network integrates the Convolutional Block Attention Module (CBAM) and Global Average Pooling (GAP) modules to optimize the capture of both local and global features. Additionally, Batch Normalization (BN) layers and Dropout techniques are incorporated to improve model stability and generalization. CAGNet was evaluated on two standard datasets, FER2013 and CK+, and the experiment results demonstrate that the network achieves accuracies of 71.52% and 97.97%, respectively, in FER. These results not only validate the effectiveness and superiority of our approach but also provide a new technical solution for FER. Furthermore, CAGNet offers robust support for the intelligent upgrade of service robots. |
| format | Article |
| id | doaj-art-8b3ba2d7bfec41c6aa48d2bc1baf6bec |
| institution | Matheson Library |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-8b3ba2d7bfec41c6aa48d2bc1baf6bec2025-06-25T14:25:23ZengMDPI AGSensors1424-82202025-06-012512365310.3390/s25123653CAGNet: A Network Combining Multiscale Feature Aggregation and Attention Mechanisms for Intelligent Facial Expression Recognition in Human-Robot InteractionDengpan Zhang0Wenwen Ma1Zhihao Shen2Qingping Ma3School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaSchool of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaSchool of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaSchool of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaThe development of Facial Expression Recognition (FER) technology has significantly enhanced the naturalness and intuitiveness of human-robot interaction. In the field of service robots, particularly in applications such as production assistance, caregiving, and daily service communication, efficient FER capabilities are crucial. However, existing Convolutional Neural Network (CNN) models still have limitations in terms of feature representation and recognition accuracy for facial expressions. To address these challenges, we propose CAGNet, a novel network that combines multiscale feature aggregation and attention mechanisms. CAGNet employs a deep learning-based hierarchical convolutional architecture, enhancing the extraction of features at multiple scales through stacked convolutional layers. The network integrates the Convolutional Block Attention Module (CBAM) and Global Average Pooling (GAP) modules to optimize the capture of both local and global features. Additionally, Batch Normalization (BN) layers and Dropout techniques are incorporated to improve model stability and generalization. CAGNet was evaluated on two standard datasets, FER2013 and CK+, and the experiment results demonstrate that the network achieves accuracies of 71.52% and 97.97%, respectively, in FER. These results not only validate the effectiveness and superiority of our approach but also provide a new technical solution for FER. Furthermore, CAGNet offers robust support for the intelligent upgrade of service robots.https://www.mdpi.com/1424-8220/25/12/3653Facial Expression RecognitionMultiscale Feature AggregationConvolutional Block Attention ModuleGlobal Average Pooling |
| spellingShingle | Dengpan Zhang Wenwen Ma Zhihao Shen Qingping Ma CAGNet: A Network Combining Multiscale Feature Aggregation and Attention Mechanisms for Intelligent Facial Expression Recognition in Human-Robot Interaction Sensors Facial Expression Recognition Multiscale Feature Aggregation Convolutional Block Attention Module Global Average Pooling |
| title | CAGNet: A Network Combining Multiscale Feature Aggregation and Attention Mechanisms for Intelligent Facial Expression Recognition in Human-Robot Interaction |
| title_full | CAGNet: A Network Combining Multiscale Feature Aggregation and Attention Mechanisms for Intelligent Facial Expression Recognition in Human-Robot Interaction |
| title_fullStr | CAGNet: A Network Combining Multiscale Feature Aggregation and Attention Mechanisms for Intelligent Facial Expression Recognition in Human-Robot Interaction |
| title_full_unstemmed | CAGNet: A Network Combining Multiscale Feature Aggregation and Attention Mechanisms for Intelligent Facial Expression Recognition in Human-Robot Interaction |
| title_short | CAGNet: A Network Combining Multiscale Feature Aggregation and Attention Mechanisms for Intelligent Facial Expression Recognition in Human-Robot Interaction |
| title_sort | cagnet a network combining multiscale feature aggregation and attention mechanisms for intelligent facial expression recognition in human robot interaction |
| topic | Facial Expression Recognition Multiscale Feature Aggregation Convolutional Block Attention Module Global Average Pooling |
| url | https://www.mdpi.com/1424-8220/25/12/3653 |
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