  Deep Neural Networks (DNNs) have shown to outperform traditional methods in
various visual recognition tasks including Facial Expression Recognition (FER).
In spite of efforts made to improve the accuracy of FER systems using DNN,
existing methods still are not generalizable enough in practical applications.
This paper proposes a 3D Convolutional Neural Network method for FER in videos.
This new network architecture consists of 3D Inception-ResNet layers followed
by an LSTM unit that together extracts the spatial relations within facial
images as well as the temporal relations between different frames in the video.
Facial landmark points are also used as inputs to our network which emphasize
on the importance of facial components rather than the facial regions that may
not contribute significantly to generating facial expressions. Our proposed
method is evaluated using four publicly available databases in
subject-independent and cross-database tasks and outperforms state-of-the-art
methods.
