  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.
