Meta Transfer Learning for Facial Emotion Recognition

TL;DR

This paper introduces a transfer learning method using PathNet for facial emotion recognition, aiming to improve generalization across datasets despite limited data, and demonstrates superior performance over existing methods.

Contribution

The paper proposes a novel transfer learning approach with PathNet for facial emotion recognition, enhancing knowledge transfer between datasets and improving accuracy.

Findings

Improved emotion recognition accuracy on SAVEE and eNTERFACE datasets.

Significantly outperforms recent state-of-the-art fine-tuning methods.

Demonstrates robustness of the transfer learning approach.

Abstract

The use of deep learning techniques for automatic facial expression recognition has recently attracted great interest but developed models are still unable to generalize well due to the lack of large emotion datasets for deep learning. To overcome this problem, in this paper, we propose utilizing a novel transfer learning approach relying on PathNet and investigate how knowledge can be accumulated within a given dataset and how the knowledge captured from one emotion dataset can be transferred into another in order to improve the overall performance. To evaluate the robustness of our system, we have conducted various sets of experiments on two emotion datasets: SAVEE and eNTERFACE. The experimental results demonstrate that our proposed system leads to improvement in performance of emotion recognition and performs significantly better than the recent state-of-the-art schemes adopting fine-\ tuning/pre-trained approaches.