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Data augmentation by separating identity and emotion representations for emotional gait recognition

Published online by Cambridge University Press:  06 February 2023

Weijie Sheng Open the ORCID record for Weijie Sheng [Opens in a new window] ,
Xiaoyan Lu  and
Xinde Li
Weijie Sheng
Affiliation:
Yangzhou Collaborative Innovation Research Institute Co., Ltd., Institute of Shenyang Aircraft Design and Research, Yangzhou, 225000, China Key Laboratory of Measurement and Control of CSE Ministry of Education, School of Automation, Southeast University, Nanjing, China
Xiaoyan Lu
Affiliation:
School of Cyber Science and Engineering, Southeast University, Nanjing, China
Xinde Li*
Affiliation:
Key Laboratory of Measurement and Control of CSE Ministry of Education, School of Automation, Southeast University, Nanjing, China School of Cyber Science and Engineering, Southeast University, Nanjing, China
*
*Corresponding author. Email: xindeli@seu.edu.cn
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Abstract

Human-centered intelligent human–robot interaction can transcend the traditional keyboard and mouse and have the capacity to understand human communicative intentions by actively mining implicit human clues (e.g., identity information and emotional information) to meet individuals’ needs. Gait is a unique biometric feature that can provide reliable information to recognize emotions even when viewed from a distance. However, the insufficient amount and diversity of training data annotated with emotions severely hinder the application of gait emotion recognition. In this paper, we propose an adversarial learning framework for emotional gait dataset augmentation, with which a two-stage model can be trained to generate a number of synthetic emotional samples by separating identity and emotion representations from gait trajectories. To our knowledge, this is the first work to realize the mutual transformation between natural gait and emotional gait. Experimental results reveal that the synthetic gait samples generated by the proposed networks are rich in emotional information. As a result, the emotion classifier trained on the augmented dataset is competitive with state-of-the-art gait emotion recognition works.

Keywords

human–robot interactiondata augmentationgait recognitionemotion recognition
Type
Research Article
Information
Robotica , Volume 41 , Issue 5 , May 2023 , pp. 1452 - 1465
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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