Zhang Dengpan, Ma Wenwen, Shen Zhihao, Ma Qingping
School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454000, China.
Sensors (Basel). 2025 Jun 11;25(12):3653. doi: 10.3390/s25123653.
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.
面部表情识别（FER）技术的发展显著提高了人机交互的自然性和直观性。在服务机器人领域，特别是在生产辅助、护理和日常服务通信等应用中，高效的FER能力至关重要。然而，现有的卷积神经网络（CNN）模型在面部表情的特征表示和识别准确性方面仍然存在局限性。为了应对这些挑战，我们提出了CAGNet，一种结合多尺度特征聚合和注意力机制的新型网络。CAGNet采用基于深度学习的分层卷积架构，通过堆叠卷积层增强多尺度特征的提取。该网络集成了卷积块注意力模块（CBAM）和全局平均池化（GAP）模块，以优化局部和全局特征的捕获。此外，还引入了批量归一化（BN）层和随机失活（Dropout）技术来提高模型的稳定性和泛化能力。CAGNet在两个标准数据集FER2013和CK+上进行了评估，实验结果表明，该网络在FER中分别达到了71.52%和97.97%的准确率。这些结果不仅验证了我们方法的有效性和优越性，还为FER提供了一种新的技术解决方案。此外，CAGNet为服务机器人的智能升级提供了有力支持。
