Complex emotion recognition is a cognitive task that has so far eluded the same excellent performance of other tasks that are at or above the level of human cognition. Emotion recognition through facial expressions is particularly difficult due to the complexity of emotions expressed by the human face. For a machine to approach the same level of performance in this domain as a human, it may need to synthesise knowledge and understand new concepts in real-time as humans do. Humans are able to learn new concepts using only few examples, by distilling the important information from memories and discarding the rest. Similarly, continual learning methods learn new classes whilst retaining the knowledge of known classes, whilst few-shot learning methods are able to learn new classes using very few training examples. We propose a novel continual learning method inspired by human cognition and learning that can accurately recognise new compound expression classes using few training samples, by building on and retaining its knowledge of basic expression classes. Using GradCAM visualisations, we demonstrate the relationship between basic and compound facial expressions, which our method leverages through knowledge distillation and a novel Predictive Sorting Memory Replay. Our method achieves the current state-of-the-art in continual learning for complex facial expression recognition with 74.28% Overall Accuracy on new classes. We also demonstrate that using continual learning for complex facial expression recognition achieves far better performance than non-continual learning methods, improving on state-of-the-art non-continual learning methods by 13.95%. To the best of our knowledge, our work is also the first to apply few-shot learning to complex facial expression recognition, achieving the state-of-the-art with 100% accuracy using a single training sample for each expression class.

我们提出了一种新颖的连续学习方法，灵感来源于人类认知和学习，通过知识蒸馏和一种新颖的预测排序记忆重放，能准确识别使用少量训练样本的新复合表情类别。使用GradCAM可视化技术，我们展示了基本和复合面部表情之间的关系，我们的方法通过知识蒸馏和记忆重放利用这种关系。我们的方法在复杂面部表情识别的连续学习方面达到了当前的最新水平，新类别的整体准确率为74.28%。我们还证明，与非连续学习方法相比，使用连续学习实现复杂面部表情识别能够获得更好的性能，提高了13.95%的非连续学习方法的最新水平。据我们所知，我们的工作还是首次将少样本学习应用于复杂面部表情识别，每个表情类别使用单个训练样本实现了100%的准确率。

基于深度知识蒸馏的复杂面部表情识别