Abstract
Previous research has revealed that people are generally poor at distinguishing genuine and acted anger facial expressions, with a mere 65% accuracy of verbal answers. We aim to investigate whether a group of feedforward neural networks can perform better using raw pupillary dilation signals from individuals. Our results show that a single neural network cannot accurately discern the veracity of an emotion based on raw physiological signals, with an accuracy of 50.5%. Nonetheless, distinct neural networks using pupillary dilation signals from different individuals display a variety of genuineness for discerning the anger emotion, from 27.8% to 83.3%. By leveraging these differences, our novel Misaka neural networks can compose predictions using different individuals’ pupillary dilation signals to give a more accurate overall prediction than even from the highest performing single individual, reaching an accuracy of 88.9%. Further research will involve the investigation of the correlation between two groups of high-performing predictors using verbal answers and pupillary dilation signals.
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The authors acknowledge Dongyang Li, Liang Zhang and Zihan Wang for the suggestion of applying Bayes’ theorem in the probability calculation.
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Qin, Z., Gedeon, T., Chen, L., Zhu, X., Hossain, M.Z. (2018). Artificial Neural Networks Can Distinguish Genuine and Acted Anger by Synthesizing Pupillary Dilation Signals from Different Participants. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_27
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