Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on facial expression recognition. However, the performance degrades dramatically under real-world settings due to variations introduced by subtle facial appearance changes, head pose variations, illumination changes, and occlusions. In this paper, a novel island loss is proposed to enhance the discriminative power of the deeply learned features. Specifically, the IL is designed to reduce the intra-class variations while enlarging the inter-class differences simultaneously. Experimental results on four benchmark expression databases have demonstrated that the CNN with the proposed island loss (IL-CNN) outperforms the baseline CNN models with either traditional softmax loss or the center loss and achieves comparable or better performance compared with the state-of-the-art methods for facial expression recognition.

本文通过引入新的岛津损失函数，在人脸表情识别领域尝试了新的改进方法，从而提高了卷积神经网络的性能。实验结果显示，使用该岛津损失函数的卷积神经网络相比于传统的softmax loss和中心损失方法以及同领域最先进方法表现更优，成功解决了真实世界条件下人脸表情识别中的问题。

面部表情识别中基于岛屿损失的判别特征学习