Abstract
Anger is an important emotion in social interactions. People can be angry from the feeling, or by acting, with an aim to turn situations to their advantage. With advances in affective computing, machine learning based approaches make it possible to identify veracity of anger through physiological signals of observers. In this paper, we examine time-series pupillary responses of observers viewing genuine and acted anger stimuli. A Fully-Connected Neural Network (FCNN) and an Long-Short Term Memory (LSTM) are trained using pre-processed pupillary responses to classify genuine anger and acted anger expressed from the stimuli. We also adopt the Bimodal Distribution Removal (BDR) technique to remove noise from the dataset. We find that both FCNN and LSTM can recognise veracity of anger with an accuracy of \(79.7\%\) and \(89.7\%\) respectively. The use of BDR is beneficial in providing an early stopping for LSTM to avoid overfitting and improve efficiency.
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Jin, R., Zhu, X., Fu, YS. (2020). Identifying Anger Veracity Using Neural Network and Long-Short Term Memory with Bimodal Distribution Removal. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_27
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DOI: https://doi.org/10.1007/978-3-030-63820-7_27
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