Dynamic Facial Expression Understanding Using Deep Spatiotemporal LDSP On Spark
Facial expressions are the most common medium for expressing human emotions. Due to the wide range of real-world applications, facial expression understanding has received extensive attention from researchers. One of the most vital issues of facial expression recognition is the extraction and modeli...
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doaj-92638d152c8644f38f56ac506e7e6aad2021-03-30T15:15:48ZengIEEEIEEE Access2169-35362021-01-019168661687710.1109/ACCESS.2021.30532769330541Dynamic Facial Expression Understanding Using Deep Spatiotemporal LDSP On SparkMd Azher Uddin0https://orcid.org/0000-0002-7718-5627Joolekha Bibi Joolee1https://orcid.org/0000-0001-5037-7672Kyung-Ah Sohn2https://orcid.org/0000-0001-8941-1188Department of Artificial Intelligence, Ajou University, Suwon, Republic of KoreaDepartment of Computer Science and Engineering, Kyung Hee University Global Campus, Yongin, Republic of KoreaDepartment of Artificial Intelligence, Ajou University, Suwon, Republic of KoreaFacial expressions are the most common medium for expressing human emotions. Due to the wide range of real-world applications, facial expression understanding has received extensive attention from researchers. One of the most vital issues of facial expression recognition is the extraction and modeling of the temporal dynamics of facial emotions from videos. Additionally, the rapid growth of video data from various multimedia sources is becoming a serious concern. Therefore, to address these issues, in this paper, we introduce a novel approach on top of Spark for facial expression understanding from videos. First, we propose a new dynamic feature descriptor, namely, the local directional structural pattern from three orthogonal planes (LDSP-TOP), which analyzes the structural aspects of the local dynamic texture. Second, we design a 1-D convolutional neural network (CNN) to capture additional discriminative features. Third, a long short-term memory (LSTM) autoencoder is employed to learn the spatiotemporal features. Finally, an extensive experimental investigation is carried out to demonstrate the performance and scalability of the proposed framework.https://ieeexplore.ieee.org/document/9330541/Facial expression understandinglocal directional structural pattern from three orthogonal planes1-D CNNLSTM autoencoder |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Md Azher Uddin Joolekha Bibi Joolee Kyung-Ah Sohn |
spellingShingle |
Md Azher Uddin Joolekha Bibi Joolee Kyung-Ah Sohn Dynamic Facial Expression Understanding Using Deep Spatiotemporal LDSP On Spark IEEE Access Facial expression understanding local directional structural pattern from three orthogonal planes 1-D CNN LSTM autoencoder |
author_facet |
Md Azher Uddin Joolekha Bibi Joolee Kyung-Ah Sohn |
author_sort |
Md Azher Uddin |
title |
Dynamic Facial Expression Understanding Using Deep Spatiotemporal LDSP On Spark |
title_short |
Dynamic Facial Expression Understanding Using Deep Spatiotemporal LDSP On Spark |
title_full |
Dynamic Facial Expression Understanding Using Deep Spatiotemporal LDSP On Spark |
title_fullStr |
Dynamic Facial Expression Understanding Using Deep Spatiotemporal LDSP On Spark |
title_full_unstemmed |
Dynamic Facial Expression Understanding Using Deep Spatiotemporal LDSP On Spark |
title_sort |
dynamic facial expression understanding using deep spatiotemporal ldsp on spark |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
Facial expressions are the most common medium for expressing human emotions. Due to the wide range of real-world applications, facial expression understanding has received extensive attention from researchers. One of the most vital issues of facial expression recognition is the extraction and modeling of the temporal dynamics of facial emotions from videos. Additionally, the rapid growth of video data from various multimedia sources is becoming a serious concern. Therefore, to address these issues, in this paper, we introduce a novel approach on top of Spark for facial expression understanding from videos. First, we propose a new dynamic feature descriptor, namely, the local directional structural pattern from three orthogonal planes (LDSP-TOP), which analyzes the structural aspects of the local dynamic texture. Second, we design a 1-D convolutional neural network (CNN) to capture additional discriminative features. Third, a long short-term memory (LSTM) autoencoder is employed to learn the spatiotemporal features. Finally, an extensive experimental investigation is carried out to demonstrate the performance and scalability of the proposed framework. |
topic |
Facial expression understanding local directional structural pattern from three orthogonal planes 1-D CNN LSTM autoencoder |
url |
https://ieeexplore.ieee.org/document/9330541/ |
work_keys_str_mv |
AT mdazheruddin dynamicfacialexpressionunderstandingusingdeepspatiotemporalldsponspark AT joolekhabibijoolee dynamicfacialexpressionunderstandingusingdeepspatiotemporalldsponspark AT kyungahsohn dynamicfacialexpressionunderstandingusingdeepspatiotemporalldsponspark |
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