  A novel procedure is presented in this paper, for training a deep
convolutional and recurrent neural network, taking into account both the
available training data set and some information extracted from similar
networks trained with other relevant data sets. This information is included in
an extended loss function used for the network training, so that the network
can have an improved performance when applied to the other data sets, without
forgetting the learned knowledge from the original data set. Facial expression
and emotion recognition in-the-wild is the test bed application that is used to
demonstrate the improved performance achieved using the proposed approach. In
this framework, we provide an experimental study on categorical emotion
recognition using datasets from a very recent related emotion recognition
challenge.
