Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation
This work addresses facial expression recognition for applications like human-computer interaction, but it is incremental as it builds on existing methods with hybrid techniques.
The paper tackles the problem of overfitting in facial expression recognition due to small datasets and intra-class variability by proposing a method that learns an optimized compact network topology using localized facial landmark features, achieving performance comparable to video-based state-of-the-art methods with much less complexity on three widely used datasets.
Deep neural networks have been widely used for feature learning in facial expression recognition systems. However, small datasets and large intra-class variability can lead to overfitting. In this paper, we propose a method which learns an optimized compact network topology for real-time facial expression recognition utilizing localized facial landmark features. Our method employs a spatio-temporal bilinear layer as backbone to capture the motion of facial landmarks during the execution of a facial expression effectively. Besides, it takes advantage of Monte Carlo Dropout to capture the model's uncertainty which is of great importance to analyze and treat uncertain cases. The performance of our method is evaluated on three widely used datasets and it is comparable to that of video-based state-of-the-art methods while it has much less complexity.