Facial expression recognition via ResNet-50

Li, Bin; Lima, Dimas

doi:10.1016/j.ijcce.2021.02.002

Cited by 144 publications

(55 citation statements)

References 27 publications

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“…Here, NWL represents the number of weighted layers and HS hyperparameter setting. Transfer learning, such as ResNet-50 ( 29 ), may help quickly build the network. In our study, we find ResNet-50 and other pretrained models do not provide competitive performances as building networks from scratch, which is coherent with the reports in ( 20 ).…”

Section: Methodsmentioning

confidence: 99%

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RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

Wang

Satapathy

Anderson

et al. 2021

Front. Public Health

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Aim: Coronavirus disease 2019 (COVID-19) is a form of disease triggered by a new strain of coronavirus. This paper proposes a novel model termed “deep fractional max pooling neural network (DFMPNN)” to diagnose COVID-19 more efficiently.Methods: This 12-layer DFMPNN replaces max pooling (MP) and average pooling (AP) in ordinary neural networks with the help of a novel pooling method called “fractional max-pooling” (FMP). In addition, multiple-way data augmentation (DA) is employed to reduce overfitting. Model averaging (MA) is used to reduce randomness.Results: We ran our algorithm on a four-category dataset that contained COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis (SPT), and healthy control (HC). The 10 runs on the test set show that the micro-averaged F1 (MAF) score of our DFMPNN is 95.88%.Discussions: This proposed DFMPNN is superior to 10 state-of-the-art models. Besides, FMP outperforms traditional MP, AP, and L2-norm pooling (L2P).

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Section: Methodsmentioning

confidence: 99%

“…, X 1 , andK x r hm (i) , x = 1, • • • , X 1 are combined to form a new enhanced dataset D (i). See Equation (29).…”

Section: Import Raw Image R(i)mentioning

confidence: 99%

RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

Wang

Satapathy

Anderson

et al. 2021

Front. Public Health

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“…When designing the network, ResNeSt-50 [13] was used as the basic framework of the network to ensure that detailed information could be extracted. Compared with ResNet-50 [23], the essence of the improvement of ResNeSt-50 is the introduction of the split-attention module, which captures the relationship across channels through a channelbased attention mechanism. ResNeSt has achieved excellent results in image classification, object detection, instance segmentation, and semantic segmentation tasks.…”

Section: Methodsmentioning

confidence: 99%

Mathematical Formula Image Screening Based on Feature Correlation Enhancement

et al. 2022

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There are mathematical formula images or other images in scientific and technical documents or on web pages, and mathematical formula images are classified as either containing only mathematical formulas or formulas interspersed with other elements, such as text and coordinate diagrams. To screen and collect images containing mathematical formulas for others to study or for further research, a model for screening images of mathematical formulas based on feature correlation enhancement is proposed. First, the Feature Correlation Enhancement (FCE) module was designed to improve the correlation degree of mathematical formula features and weaken other features. Then, the strip multi-scale pooling (SMP) module was designed to solve the problem of non-uniform image size, while enhancing the focus on horizontal formula features. Finally, the loss function was improved to balance the dataset. The accuracy of the experiment was 89.50%, which outperformed the existing model. Using the model to screen images enables the user to screen out images containing mathematical formulas. The screening of images containing mathematical formulas helps to speed up the creation of a database of mathematical formula images.

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“…After that, facial and speech emotions were fused using a weighted decision fusion method. Li et al [29] applied ResNet-50 to capture the facial emotions of humans. This helped to improve the robustness and generalization ability of the recognition models.…”

Section: Related Workmentioning

confidence: 99%

Efficient facial emotion recognition model using deep convolutional neural network and modified joint trilateral filter

Kumari

Bhatia

2022

Soft Comput

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Facial emotion recognition extracts the human emotions from the images and videos. As such, it requires an algorithm to understand and model the relationships between faces and facial expressions, and to recognize human emotions. Recently, deep learning models are extensively utilized enhance the facial emotion recognition rate. However, the deep learning models suffer from the overfitting issue. Moreover, deep learning models perform poorly for images which have poor visibility and noise. Therefore, in this paper, a novel deep learning based facial emotion recognition tool is proposed. Initially, a joint trilateral filter is applied to the obtained dataset to remove the noise. Thereafter, contrast-limited adaptive histogram equalization (CLAHE) is applied to the filtered images to improve the visibility of images. Finally, a deep convolutional neural network is trained. Nadam optimizer is also utilized to optimize the cost function of deep convolutional neural networks. Experiments are achieved by using the benchmark dataset and competitive human emotion recognition models. Comparative analysis demonstrates that the proposed facial emotion recognition model performs considerably better compared to the competitive models.

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Facial expression recognition via ResNet-50

Cited by 144 publications

References 27 publications

RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

Mathematical Formula Image Screening Based on Feature Correlation Enhancement

Efficient facial emotion recognition model using deep convolutional neural network and modified joint trilateral filter

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