Advertisement
Published in:
2020 | OriginalPaper | Chapter
Identifying Anger Veracity Using Neural Network and Long-Short Term Memory with Bimodal Distribution Removal
Authors : Rouyi Jin, Xuanying Zhu, Yeu-Shin Fu
Published in: Neural Information Processing
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.