  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 complex facial expression
recognition 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 important information from memories.
Inspired by human cognition and learning, we propose a novel continual learning
method for complex facial expression recognition that can accurately recognise
new compound expression classes using few training samples, by building on and
retaining its knowledge of basic expression classes. In this work, we also use
GradCAM visualisations to demonstrate the relationship between basic and
compound facial expressions. Our method leverages this relationship through
knowledge distillation and a novel Predictive Sorting Memory Replay, to achieve
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%. 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
only a single training sample per class.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning