A Genetic Feature Selection Based Two-Stream Neural Network for Anger Veracity Recognition

Chaoxing Huang*, Xuanying Zhu, Tom Gedeon

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Citation (Scopus)

    Abstract

    People can manipulate emotion expressions when interacting with others. For example, acted anger can be expressed when the stimulus is not genuinely angry with an aim to manipulate the observer. In this paper, we aim to examine if the veracity of anger can be recognized from observers’ pupillary data with computational approaches. We use Genetic-based Feature Selection (GFS) methods to select time-series pupillary features of observers who see acted and genuine anger as video stimuli. We then use the selected features to train a simple fully connected neural network and a two-stream neural network. Our results show that the two-stream architecture is able to achieve a promising recognition result with an accuracy of 93.6% when the pupillary responses from both eyes are available. It also shows that genetic algorithm based feature selection method can effectively improve the classification accuracy by 3.1%. We hope our work could help current research such as human machine interaction and psychology studies that require emotion recognition.

    Original languageEnglish
    Title of host publicationNeural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
    EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages3-11
    Number of pages9
    ISBN (Print)9783030638290
    DOIs
    Publication statusPublished - 2020
    Event27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand
    Duration: 18 Nov 202022 Nov 2020

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume12532 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference27th International Conference on Neural Information Processing, ICONIP 2020
    Country/TerritoryThailand
    CityBangkok
    Period18/11/2022/11/20

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