为解决人脸表情识别任务中存在的类内表情差异性大、类间表情相似度高的问题,基于传统的Softmax损失函数和Island损失函数,提出一种新的基于余弦距离损失函数来指导深度卷积神经网络的学习。该方法不仅可以减小特征空间中类内特征的差异,而且可以增大类间特征分布,从而提升特征判别效果。经过大量的实验和分析,该算法在RAF-DB人脸表情数据集上的准确率达到了83.196%,效果优于Softmax损失函数和Island损失函数,所提算法在人脸表情识别任务中具有较高的优越性。

This study proposes a new cosine distance loss function based on the traditional Softmax loss function and Island loss function to guide the learning of deep convolution neural networks and solve the problem of large difference in intra-class expressions and high similarity in inter-class expressions in the facial expression recognition tasks. The proposed method not only reduces the difference of intra-class features in the feature space, but also increases the distribution of inter-class features, thereby improving the effect of feature discrimination. After conducting several experiments and analyses, the accuracy of the facial expression recognition algorithm is observed to be 83.196% based on the RAF-DB facial expression dataset, and the effect is better than those obtained using the Softmax loss function and the Island loss function. Furthermore, the proposed algorithm is highly superior with respect to the facial expression recognition tasks.