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A novel procedure is presented in this paper, for training a deepconvolutional and recurrent neural network, taking into account both theavailable training data set and some information extracted from similarnetworks trained with other relevant data sets. This information is included inan extended loss function used for the network training, so that the networkcan have an improved performance when applied to the other data sets, withoutforgetting the learned knowledge from the original data set. Facial expressionand emotion recognition in-the-wild is the test bed application that is used todemonstrate the improved performance achieved using the proposed approach. Inthis framework, we provide an experimental study on categorical emotionrecognition using datasets from a very recent related emotion recognitionchallenge.

Training Deep Neural Networks with Different Datasets In-the-wild: The Emotion Recognition Paradigm