Aiming at the problem of facial expression recognition under unconstrained conditions, a facial expression recognition method based on an improved capsule network model is proposed. Firstly, the expression image is normalized by illumination based on the improved Weber face, and the key points of the face are detected by the Gaussian process regression tree. Then, the 3dmms model is introduced. The 3D face shape, which is consistent with the face in the image, is provided by iterative estimation so as to further improve the image quality of face pose standardization. In this paper, we consider that the convolution features used in facial expression recognition need to be trained from the beginning and add as many different samples as possible in the training process. Finally, this paper attempts to combine the traditional deep learning technology with capsule configuration, adds an attention layer after the primary capsule layer in the capsule network, and proposes an improved capsule structure model suitable for expression recognition. The experimental results on JAFFE and BU-3DFE datasets show that the recognition rate can reach 96.66% and 80.64%, respectively.

中文翻译：

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一种基于改进胶囊网络模型的面部表情识别方法

针对无约束条件下的人脸表情识别问题，提出了一种基于改进胶囊网络模型的人脸表情识别方法。首先基于改进的韦伯人脸对表情图像进行光照归一化，并通过高斯过程回归树检测人脸的关键点。然后，介绍了3dmms模型。通过迭代估计提供与图像中人脸一致的3D人脸形状，以进一步提高人脸姿态标准化的图像质量。在本文中，我们认为面部表情识别中使用的卷积特征需要从一开始就进行训练，并在训练过程中加入尽可能多的不同样本。最后，本文尝试将传统的深度学习技术与胶囊配置相结合，在胶囊网络中的初级胶囊层之后增加一个注意力层，提出了一种适用于表情识别的改进的胶囊结构模型。在 JAFFE 和 BU-3DFE 数据集上的实验结果表明，识别率可以分别达到 96.66% 和 80.64%。