An Evolutionary-based Neural Network for Distinguishing between Genuine and Posed Anger from Observers' Pupillary Responses

Abstract

Future human-computing research could be enhanced by recognizing attitude/emotion (for example, anger) from observers' reactions (for example, pupillary responses). This paper analyzes observers' pupillary responses by developing neural network (NN) models to distinguish between genuine and posed anger. Any model's relatively high classification accuracy means the pupillary responses and observed anger (genuine or posed) are deeply connected. In this connection, we implemented strategies for tuning parameters of the model, methods to optimize and compress the model structure, analyze the similarity of hidden units, and decide which of them should be removed. We achieved the goal of removing the network's redundant neurons without significant performance decline and improved the training speed. Finally, our evolutionary-based NN model showed the highest accuracy of 86% with a 3-layers structure and outperformed the backpropagation-based NN. The high accuracy highlights the potential of our model to use in the future for distinguishing observers' reactions to emotion/attitude recognition.