  In the context of artificial intelligence, the inherent human attribute of
engaging in logical reasoning to facilitate decision-making is mirrored by the
concept of explainability, which pertains to the ability of a model to provide
a clear and interpretable account of how it arrived at a particular outcome.
This study explores explainability techniques for binary deep neural
architectures in the framework of emotion classification through video
analysis. We investigate the optimization of input features to binary
classifiers for emotion recognition, with face landmarks detection using an
improved version of the Integrated Gradients explainability method. The main
contribution of this paper consists in the employment of an innovative
explainable artificial intelligence algorithm to understand the crucial facial
landmarks movements during emotional feeling, using this information also for
improving the performances of deep learning-based emotion classifiers. By means
of explainability, we can optimize the number and the position of the facial
landmarks used as input features for facial emotion recognition, lowering the
impact of noisy landmarks and thus increasing the accuracy of the developed
models. In order to test the effectiveness of the proposed approach, we
considered a set of deep binary models for emotion classification trained
initially with a complete set of facial landmarks, which are progressively
reduced based on a suitable optimization procedure. The obtained results prove
the robustness of the proposed explainable approach in terms of understanding
the relevance of the different facial points for the different emotions, also
improving the classification accuracy and diminishing the computational cost.
