  Light field (LF) cameras provide rich spatio-angular visual representations
by sensing the visual scene from multiple perspectives and have recently
emerged as a promising technology to boost the performance of human-machine
systems such as biometrics and affective computing. Despite the significant
success of LF representation for constrained facial image analysis, this
technology has never been used for face and expression recognition in the wild.
In this context, this paper proposes a new deep face and expression recognition
solution, called CapsField, based on a convolutional neural network and an
additional capsule network that utilizes dynamic routing to learn hierarchical
relations between capsules. CapsField extracts the spatial features from facial
images and learns the angular part-whole relations for a selected set of 2D
sub-aperture images rendered from each LF image. To analyze the performance of
the proposed solution in the wild, the first in the wild LF face dataset, along
with a new complementary constrained face dataset captured from the same
subjects recorded earlier have been captured and are made available. A subset
of the in the wild dataset contains facial images with different expressions,
annotated for usage in the context of face expression recognition tests. An
extensive performance assessment study using the new datasets has been
conducted for the proposed and relevant prior solutions, showing that the
CapsField proposed solution achieves superior performance for both face and
expression recognition tasks when compared to the state-of-the-art.
