Learning Gait Emotions Using Affective and Deep Features-Reference-Cited by-同舟云学术

Learning Gait Emotions Using Affective and Deep Features

Published:2022-11-03 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 15th ACM SIGGRAPH Conference on Motion, Interaction and Games
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Author:

Randhavane Tanmay¹,Bhattacharya Uttaran²,Kabra Pooja²,Kapsaskis Kyra¹,Gray Kurt¹,Manocha Dinesh²,Bera Aniket³

Affiliation:

1. University of North Carolina at Chapel Hill, USA

2. University of Maryland, USA

3. Purdue University, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3561975.3562957

Reference68 articles.

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2. Danilo Avola , Marco Cascio , Luigi Cinque , Gian Luca Foresti , Cristiano Massaroni , and Emanuele Rodolà . 2019. 2d skeleton-based action recognition via two-branch stacked lstm-rnns . IEEE Transactions on Multimedia( 2019 ). Danilo Avola, Marco Cascio, Luigi Cinque, Gian Luca Foresti, Cristiano Massaroni, and Emanuele Rodolà. 2019. 2d skeleton-based action recognition via two-branch stacked lstm-rnns. IEEE Transactions on Multimedia(2019).

3. LF Barrett 2019. Emotional expressions reconsidered: challenges to inferring emotion from human facial movements. Psychological Science in the Public Interest ( 2019 ). LF Barrett 2019. Emotional expressions reconsidered: challenges to inferring emotion from human facial movements. Psychological Science in the Public Interest (2019).

4. F C Benitez-Quiroz , R Srinivasan , 2016 . Emotionet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild. In CVPR. F C Benitez-Quiroz, R Srinivasan, 2016. Emotionet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild. In CVPR.

5. Detecting Affect from Non-stylised Body Motions

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