Facial Expression Recognition for Human Computer Interaction

Chattopadhyay, Joyati; Kundu, Souvik; Chakraborty, Arpita; Banerjee, Jyoti Sekhar

doi:10.1007/978-3-030-41862-5_119

Cited by 20 publications

(2 citation statements)

References 24 publications

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“…The task of measuring the intensities of the six basic emotions from facial expression images has many important real-world applications, including human-computer interaction [8], prediction of depression levels in patients [9], recognition of intruders [10], understanding students' nonverbal behaviors [11], and security screening in crowded places [12]. This task can be addressed by label-distribution learning (LDL) [6], [7], [13].…”

Section: Introductionmentioning

confidence: 99%

Visibility Convolutional Neural Networks for Label Distribution Learning of Facial Emotions

Almowallad,

Sanchez

2023

Preprint

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Section: Introductionmentioning

confidence: 99%

Visibility Convolutional Neural Networks for Label Distribution Learning of Facial Emotions

Almowallad,

Sanchez

2023

Preprint

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“…A study by psychologist Mehrabian revealed that language only accounts for 7% of the information expressed in interpersonal interactions, whereas facial expressions express as much as 55%. This suggests that facial expressions play a significant role in human emotion judgment [1] and can be applied in a variety of scenarios, including medical treatment [2,3], fatigue driving detection [4][5][6][7], social robots [8][9][10], and other human-computer interaction systems [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network–Bidirectional Gated Recurrent Unit Facial Expression Recognition Method Fused with Attention Mechanism

Tang,

Zhang,

Tian

2023

Applied Sciences

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The relationships among different subregions in facial images and their varying contributions to facial expression recognition indicate that using a fixed subregion weighting scheme would result in a substantial loss of valuable information. To address this issue, we propose a facial expression recognition network called BGA-Net, which combines bidirectional gated recurrent units (BiGRUs) with an attention mechanism. Firstly, a convolutional neural network (CNN) is employed to extract feature maps from facial images. Then, a sliding window cropping strategy is applied to divide the feature maps into multiple subregions. The BiGRUs are utilized to capture the dependencies among these subregions. Finally, an attention mechanism is employed to adaptively focus on the most discriminative regions. When evaluated on CK+, FER2013, and JAFFE datasets, our proposed method achieves promising results.

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South Asian Countries Are Less Fatal Concerning COVID-19: A Hybrid Approach Using Machine Learning and M-AHP

Guhathakurata¹,

Saha²,

Kundu

et al. 2021

EAI/Springer Innovations in Communication and Computing

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Facial Expression Recognition for Human Computer Interaction

Cited by 20 publications

References 24 publications

Visibility Convolutional Neural Networks for Label Distribution Learning of Facial Emotions

Visibility Convolutional Neural Networks for Label Distribution Learning of Facial Emotions

Convolutional Neural Network–Bidirectional Gated Recurrent Unit Facial Expression Recognition Method Fused with Attention Mechanism

South Asian Countries Are Less Fatal Concerning COVID-19: A Hybrid Approach Using Machine Learning and M-AHP

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