Over the past few years, neural networks have made a huge improvement in object recognition and event analysis. However, due to a lack of available data, neural networks were not efficiently applied in expression analysis. In this paper, we tackle the problem of facial expression analysis using deep neural network by generating a realistic large scale synthetic labeled dataset. We train a deep 3-dimensional convolutional network on the generated dataset and empirically show how the presented method can efficiently classify facial expressions. Our method addresses four fundamental issues: (i) generating a large scale facial expression dataset that is realistic and accurate, (ii) a rich spatial representation of expressions, (iii) better spatiotemporal feature learning compared to recent techniques and (iv) with a simple linear classifier our learned features outperform state-of-the-art 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.
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