为了解决深度学习模型在人脸表情识别研究中存在的数据集需求量大、硬件配置要求高等问题,提出了一种基于卷积注意力的轻量级人脸表情识别方法。首先,用分解卷积对模型参数进行降维处理;然后,在模型中嵌入卷积注意力机制模块,以提高模型的特征提取能力;其次,针对数据集中的类别不平衡问题,采取代价敏感的损失函数对模型进行优化;最后,进行表情识别任务前将模型在人脸识别数据集上进行预训练,以提高模型提取人脸特征的能力。实验结果表明,本方法能在有效降低模型复杂度的同时保持较高水平的检测效果,且具有较强的实用性。

In the research on facial expression recognition of deep learning models, a lightweight facial expression recognition method based on convolutional attention is proposed in this paper to solve the problems of large dataset demand and high hardware configuration requirements. First, the model parameters are decomposed and convolved for dimensionality reduction. Then, the convolutional attention mechanism module is embedded in the model to improve its feature extraction ability. For the problem of category imbalance in a dataset, the model is optimized using a cost-sensitive loss function. Finally, the model is pretrained on a face recognition dataset before performing a facial expression recognition task to improve the model’s ability of extracting facial features. Experiment results show that the method effectively reduced the model complexity while maintaining a high level of detection effect along with having strong practicability.