Meaningful Learning for Deep Facial Emotional Features

Filali, Hajar; Riffi, Jamal; Aboussaleh, Ilyasse; Mahraz, Mohamed Adnane; Tairi, Hamid

doi:10.1007/s11063-021-10636-1

Cited by 7 publications

(2 citation statements)

References 46 publications

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“…The sample size of disgust and fear expressions may be too small and insensitive to determine disgust and fear expressions. CK+ Comparison: For the CK+ dataset, we compared our method with VGG Net+LSTM [48], SLPM [49], Pre-trained CNN [50], GA-SVM [51], PyFER [52], AC-GAN [53], CNN+SAE [54], and CMCNN [55] methods. The comparison results are shown in Table 3, in which our method achieved 99.66% accuracy using the CK+ dataset.…”

Section: Methods Year Accuracy (%)mentioning

confidence: 99%

Facial Expression Recognition Methods in the Wild Based on Fusion Feature of Attention Mechanism and LBP

Lei

Lin

et al. 2023

Sensors

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Facial expression methods play a vital role in human–computer interaction and other fields, but there are factors such as occlusion, illumination, and pose changes in wild facial recognition, as well as category imbalances between different datasets, that result in large variations in recognition rates and low accuracy rates for different categories of facial expression datasets. This study introduces RCL-Net, a method of recognizing wild facial expressions that is based on an attention mechanism and LBP feature fusion. The structure consists of two main branches, namely the ResNet-CBAM residual attention branch and the local binary feature (LBP) extraction branch (RCL-Net). First, by merging the residual network and hybrid attention mechanism, the residual attention network is presented to emphasize the local detail feature information of facial expressions; the significant characteristics of facial expressions are retrieved from both channel and spatial dimensions to build the residual attention classification model. Second, we present a locally improved residual network attention model. LBP features are introduced into the facial expression feature extraction stage in order to extract texture information on expression photographs in order to emphasize facial feature information and enhance the recognition accuracy of the model. Lastly, experimental validation is performed using the FER2013, FERPLUS, CK+, and RAF-DB datasets, and the experimental results demonstrate that the proposed method has superior generalization capability and robustness in the laboratory-controlled environment and field environment compared to the most recent experimental methods.

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Section: Methods Year Accuracy (%)mentioning

confidence: 99%

Facial Expression Recognition Methods in the Wild Based on Fusion Feature of Attention Mechanism and LBP

Lei

Lin

et al. 2023

Sensors

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show abstract

“…In recent years, with the rapid development of artificial intelligence algorithms, and the accumulation of raw data in various disciplines is increasing. Deep learning algorithms are now widely employed in mechanical 12 , biomedical 13,14 , civil engineering 15,16 , materials 17 , and other sectors 18 , allowing for more efficient problem-solving in a variety of fields. In the field of mechanical smart manufacturing, digital twins are combined with reinforcement learning to extend to more complex manufacturing systems, using deep neural networks to solve the challenges posed by large state and action spaces 19 .…”

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confidence: 99%

Non-destructive monitoring of forming quality of self-piercing riveting via a lightweight deep learning

Lin

Zhao

Wang

et al. 2023

Sci Rep

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Self-piercing riveting (SPR) has been widely used in automobile body jointing. However, the riveting process is prone to various forming quality failures, such as empty riveting, repeated riveting, substrate cracking, and other riveting defects. This paper combines deep learning algorithms to achieve non-contact monitoring of SPR forming quality. And a lightweight convolutional neural network with higher accuracy and less computational effort is designed. The ablation and comparative experiments results show that the lightweight convolutional neural network proposed in this paper achieves improved accuracy and reduced computational complexity. Compared with the original algorithm, the algorithm’s accuracy in this paper is increased by 4.5$$\%$$ % , and the recall is increased by 1.4$$\%$$ % . In addition, the amount of redundant parameters is reduced by 86.5$$\%$$ % , and the amount of computation is reduced by 47.33$$\%$$ % . This method can effectively overcome the limitations of low efficiency, high work intensity, and easy leakage of manual visual inspection methods and provide a more efficient solution for monitoring the quality of SPR forming quality.

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FL-CapsNet: facial localization augmented capsule network for human emotion recognition

Bellamkonda

Gopalan

Mala

et al. 2022

SIViP

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Meaningful Learning for Deep Facial Emotional Features

Cited by 7 publications

References 46 publications

Facial Expression Recognition Methods in the Wild Based on Fusion Feature of Attention Mechanism and LBP

Facial Expression Recognition Methods in the Wild Based on Fusion Feature of Attention Mechanism and LBP

Non-destructive monitoring of forming quality of self-piercing riveting via a lightweight deep learning

FL-CapsNet: facial localization augmented capsule network for human emotion recognition

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