  Long Short-Term Memory (LSTM) is a prominent recurrent neural network for
extracting dependencies from sequential data such as time-series and multi-view
data, having achieved impressive results for different visual recognition
tasks. A conventional LSTM network can learn a model to posteriorly extract
information from one input sequence. However, if two or more dependent
sequences of data are simultaneously acquired, the conventional LSTM networks
may only process those sequences consecutively, not taking benefit of the
information carried out by their mutual dependencies. In this context, this
paper proposes two novel LSTM cell architectures that are able to jointly learn
from multiple sequences simultaneously acquired, targeting to create richer and
more effective models for recognition tasks. The efficacy of the novel LSTM
cell architectures is assessed by integrating them into deep learning-based
methods for face recognition with multi-view, light field images. The new cell
architectures jointly learn the scene horizontal and vertical parallaxes
available in a light field image, to capture richer spatio-angular information
from both directions. A comprehensive evaluation, with the IST-EURECOM LFFD
dataset using three challenging evaluation protocols, shows the advantage of
using the novel LSTM cell architectures for face recognition over the
state-of-the-art light field-based methods. These results highlight the added
value of the novel cell architectures when learning from correlated input
sequences.
