Authors:
Fan Wu
1
;
Md Rakibul Hasan
2
and
Md Zakir Hossain
3
;
4
Affiliations:
1
School of Computing, Biological Data Science Institute, Australian National University, Canberra ACT 2600, Australia
;
2
Department of Electrical and Electronic Engineering, BRAC University, Dhaka 1212, Bangladesh
;
3
Biological Data Science Institute, School of Biology, Australian National University, Canberra ACT 2600, Australia
;
4
CSIRO Agriculture & Food, Black Mountain, Canberra ACT 2600, Australia
Keyword(s):
Neural Network, Evolutionary Algorithm, Neural Network Pruning, Anger Veracity, Pupillary Response.
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.
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