Rafique, S, Kanwal, N ORCID: https://orcid.org/0000-0002-9732-3126, Ansari, MS, Asghar, M and Akhtar, Z (2021) Deep Learning based Emotion Classification with Temporal Pupillometry Sequences. In: 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), 09-10 December 2021, Cape Town, South Africa.

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Abstract

In the recent era, automatic systems are the necessity of science. Systems for recognizing human emotions have gained popularity in various areas of knowledge specifically psychologists and psycho-physiologists. The interaction of the human-computer using physiological signals is the precise parameter for the recognition of emotion. However, pupillometry was used in this study as an unintentional direct brain response to capture human emotions using in-depth learning. Deep learning concepts using LSTM (Long Short Term Memory) were used in this study to classify emotions. Time series data for two emotions i.e. disgust and fear were used after the pre-treatment phase and subsequently proposed a classifier for the recognition of emotions.

Item Type: Conference or Workshop Item (Paper)
Additional Information: All information related to this article, including copyrights, can be found on the publisher website.
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 22 Jul 2022 08:10
Last Modified: 22 Jul 2022 08:10
URI: https://eprints.keele.ac.uk/id/eprint/11073

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