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• W4211169968 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." @default.
• W4211169968 created "2022-02-13" @default.
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• W4211169968 date "2021-12-09" @default.
• W4211169968 modified "2023-10-18" @default.
• W4211169968 title "Deep Learning based Emotion Classification with Temporal Pupillometry Sequences" @default.
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• W4211169968 doi "https://doi.org/10.1109/icecet52533.2021.9698663" @default.
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