Noisy Student Training using Body Language Dataset Improves Facial Expression Recognition

TL;DR

This paper introduces a self-training approach using a noisy student network and multi-level attention on a body language dataset to significantly improve facial expression recognition in videos, achieving state-of-the-art results.

Contribution

The paper presents a novel self-training method with a noisy student network and multi-level attention mechanism for facial expression recognition, leveraging unlabelled data to enhance performance.

Findings

Achieves state-of-the-art results on CK+ and AFEW 8.0 datasets.

Self-training with noisy student networks improves recognition accuracy.

Multi-level attention mechanism boosts facial region processing.

Abstract

Facial expression recognition from videos in the wild is a challenging task due to the lack of abundant labelled training data. Large DNN (deep neural network) architectures and ensemble methods have resulted in better performance, but soon reach saturation at some point due to data inadequacy. In this paper, we use a self-training method that utilizes a combination of a labelled dataset and an unlabelled dataset (Body Language Dataset - BoLD). Experimental analysis shows that training a noisy student network iteratively helps in achieving significantly better results. Additionally, our model isolates different regions of the face and processes them independently using a multi-level attention mechanism which further boosts the performance. Our results show that the proposed method achieves state-of-the-art performance on benchmark datasets CK+ and AFEW 8.0 when compared to other single models.