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Context-aware emotion recognition in the wild using spatio-temporal and temporal-pyramid models

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Do, N-T, Kim, S-H, Yang, H-J, Lee, G-S and Yeom, S ORCID: 0000-0002-5843-101X 2021 , 'Context-aware emotion recognition in the wild using spatio-temporal and temporal-pyramid models' , Sensors, vol. 21, no. 7 , pp. 1-29 , doi: 10.3390/s21072344.

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Abstract

Emotion recognition plays an important role in human–computer interactions. Recent studies have focused on video emotion recognition in the wild and have run into difficulties related to occlusion, illumination, complex behavior over time, and auditory cues. State-of-the-art methods use multiple modalities, such as frame-level, spatiotemporal, and audio approaches. However, such methods have difficulties in exploiting long-term dependencies in temporal information, capturing contextual information, and integrating multi-modal information. In this paper, we introduce a multi-modal flexible system for video-based emotion recognition in the wild. Our system tracks and votes on significant faces corresponding to persons of interest in a video to classify seven basic emotions. The key contribution of this study is that it proposes the use of face feature extraction with context-aware and statistical information for emotion recognition. We also build two model architectures to effectively exploit long-term dependencies in temporal information with a temporal-pyramid model and a spatiotemporal model with “Conv2D+LSTM+3DCNN+Classify” architecture. Finally, we propose the best selection ensemble to improve the accuracy of multi-modal fusion. The best selection ensemble selects the best combination from spatiotemporal and temporal-pyramid models to achieve the best accuracy for classifying the seven basic emotions. In our experiment, we take benchmark measurement on the AFEW dataset with high accuracy.

Item Type: Article
Authors/Creators:Do, N-T and Kim, S-H and Yang, H-J and Lee, G-S and Yeom, S
Keywords: video emotion recognition, spatiotemporal, temporal-pyramid, best selection ensemble, facial emotion recognition
Journal or Publication Title: Sensors
Publisher: Molecular Diversity Preservation International
ISSN: 1424-8220
DOI / ID Number: 10.3390/s21072344
Copyright Information:

Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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