2020
DOI: 10.1016/j.bspc.2020.101867
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Automated emotion recognition based on higher order statistics and deep learning algorithm

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Cited by 144 publications
(47 citation statements)
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“…The effectiveness of their solution using CNN + LSTM was about 70%, depending on the technology. Work related to the automated system for the recognition of emotions, based on higher-order statistics and the deep learning algorithm was presented by Sharma et al [ 21 ]. Their solution reached an average of around 87%.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…The effectiveness of their solution using CNN + LSTM was about 70%, depending on the technology. Work related to the automated system for the recognition of emotions, based on higher-order statistics and the deep learning algorithm was presented by Sharma et al [ 21 ]. Their solution reached an average of around 87%.…”
Section: Discussionmentioning
confidence: 99%
“…Ding et al [ 12 ] proposed a model for recognising the unsafe actions of workers, using a convolution neural network (CNN) and long, short-term memory (LSTM). Sharma et al [ 21 ] also used the LSTM-based, deep learning technique to obtain emotion variations, based on the data from EEG signals. Rude et al [ 22 ] investigated the feasibility of using hidden Markov models and naive Bayes K-means for recognising worker activity in manufacturing processes using the Kinect sensor.…”
Section: Introductionmentioning
confidence: 99%
“…One early decision when working with EEG for emotion recognition is related to the number of electrodes to use. In the literature, this number varies from only 2 electrodes [ 13 , 14 ] to a maximum of 64 electrodes [ 15 , 16 ], with the most common value revolving around 32 [ 2 , 17 , 18 , 19 , 20 ]. Usually, the placement of the electrodes in the scalp is done according to the international 10–20 system.…”
Section: Background and Related Workmentioning
confidence: 99%
“…Due to the richness of human emotion expression, emotion recognition can use different combinations of multiple emotion expression materials to make judgments. In the historical process of emotion recognition research, scholars have researched from single-modal modeling (Edgar et al, 2020;Gosztolya, 2020;Panda et al, 2020), hybrid multi-modal modeling (Ayata et al, 2020;Zhang et al, 2020), to complex deep neural networks (Abdulsalam et al, 2019;Aouani and Ayed, 2020;Sharma et al, 2020). The relationship between human emotion and its multiple expressions is gradually being unearthed.…”
Section: Introductionmentioning
confidence: 99%