research-article

Personalized Emotion Recognition by Personality-Aware High-Order Learning of Physiological Signals

Authors:

Amir Gholaminejad,

Kurt KeutzerAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 15, Issue 1s

Article No.: 14, Pages 1 - 18

https://doi.org/10.1145/3233184

Published: 24 January 2019 Publication History

Abstract

Due to the subjective responses of different subjects to physical stimuli, emotion recognition methodologies from physiological signals are increasingly becoming personalized. Existing works mainly focused on modeling the involved physiological corpus of each subject, without considering the psychological factors, such as interest and personality. The latent correlation among different subjects has also been rarely examined. In this article, we propose to investigate the influence of personality on emotional behavior in a hypergraph learning framework. Assuming that each vertex is a compound tuple (subject, stimuli), multi-modal hypergraphs can be constructed based on the personality correlation among different subjects and on the physiological correlation among corresponding stimuli. To reveal the different importance of vertices, hyperedges, and modalities, we learn the weights for each of them. As the hypergraphs connect different subjects on the compound vertices, the emotions of multiple subjects can be simultaneously recognized. In this way, the constructed hypergraphs are vertex-weighted multi-modal multi-task ones. The estimated factors, referred to as emotion relevance, are employed for emotion recognition. We carry out extensive experiments on the ASCERTAIN dataset and the results demonstrate the superiority of the proposed method, as compared to the state-of-the-art emotion recognition approaches.

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Personalized Emotion Recognition by Personality-Aware High-Order Learning of Physiological Signals

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 15, Issue 1s

Special Section on Deep Learning for Intelligent Multimedia Analytics and Special Section on Multi-Modal Understanding of Social, Affective and Subjective Attributes of Data

January 2019

265 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3309769

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2019 ACM.

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Association for Computing Machinery

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Publication History

Published: 24 January 2019

Accepted: 01 June 2018

Revised: 01 April 2018

Received: 01 October 2017

Published in TOMM Volume 15, Issue 1s

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National Key R&D Program of China
Berkeley Deep Drive
National Natural Science Foundation of China
Royal Society Newton Mobility Grant
China Postdoctoral Science Foundation

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https://doi.org/10.26599/BDMA.2024.9020048
Ai HTao XLi XGan Y(2025)Modeling High-Order Relationships Between Human and Video for Emotion Recognition in Video LearningMultiMedia Modeling10.1007/978-981-96-2064-7_1(3-16)Online publication date: 9-Jan-2025
https://dl.acm.org/doi/10.1007/978-981-96-2064-7_1
Liu HLou TZhang YWu YXiao YJensen CZhang D(2024)EEG-Based Multimodal Emotion Recognition: A Machine Learning PerspectiveIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.336913073(1-29)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3369130
Wang BDong GZhao YLi R(2024)Personalized Multimodal Emotion Recognition: Integrating Temporal Dynamics and Individual Traits for Enhanced Performance2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP)10.1109/ISCSLP63861.2024.10800502(408-412)Online publication date: 7-Nov-2024
https://doi.org/10.1109/ISCSLP63861.2024.10800502
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Ramaswamy MPalaniswamy S(2024)Multimodal emotion recognition: A comprehensive review, trends, and challengesWIREs Data Mining and Knowledge Discovery10.1002/widm.156314:6Online publication date: 8-Oct-2024
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