Light field cameras are able to capture the intensity of light
rays coming from multiple directions, thus representing the
visual scene from multiple viewpoints. This paper exploits
the rich spatio-angular information available in light field
images for facial emotion recognition. In this context, a
new deep network is proposed that first extracts spatial features
using a VGG16 convolutional neural network. Then, a
Bidirectional Long Short-Term Memory (Bi-LSTM) recurrent
neural network is used to learn spatio-angular features
from viewpoint feature sequences, exploring both forward
and backward angular relationships. Additionally, an attention
mechanism allows our model to selectively focus on
the most important spatio-angular features, thus enabling a
more effective learning outcome. Finally, a fusion scheme
is adopted to obtain the emotion recognition classification
results. Comprehensive experiments have been conducted
on the IST-EURECOM Light Field Face database using two
challenging evaluation protocols, showing the superiority of
our method over the state-of-the-art.