Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic Features

TL;DR

This paper introduces a novel continual learning approach for complex facial expression recognition, leveraging knowledge distillation and memory replay to achieve state-of-the-art results with few training samples.

Contribution

It presents a new continual learning method that synthesizes basic and compound facial expressions, applying few-shot learning and knowledge distillation for improved accuracy.

Findings

Achieved 74.28% accuracy on new classes in continual learning.

Outperformed non-continual learning methods by 13.95%.

Attained 100% accuracy with only one training sample per class in few-shot learning.

Abstract

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.