Meta Transfer Learning for Facial Emotion Recognition
This work addresses the challenge of dataset scarcity for researchers and practitioners in facial emotion recognition, though it is incremental as it builds on existing transfer learning techniques.
The paper tackled the problem of poor generalization in facial emotion recognition due to limited datasets by proposing a novel transfer learning approach using PathNet, which improved performance on the SAVEE and eNTERFACE datasets and outperformed state-of-the-art fine-tuning/pre-trained methods.
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.