FERNet: A Deep CNN Architecture for Facial Expression Recognition in the Wild

Bodapati, Jyostna Devi; Srilakshmi, Uppalapati; Veeranjaneyulu, N.

doi:10.1007/s40031-021-00681-8

Cited by 32 publications

(8 citation statements)

References 23 publications

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“…Deep learning was used to detect COVID19 on CXR [21] .There were three stages. Initially, pneumonia was discovered, then COVID19 and pneumonia were discovered, and finally, the condition was diagnosed.They employed 6523 CXR images and achieved a 97 percent accuracy rate.The authors [22] reported using a patch-based CNN to diagnose COVID19 in CXR pictures with state-of-the-art accuracy.In [23] , the authors described a method for identifying diseases from COVID19 images that used COVID19 CXR image descriptors, feed-forward neural networks, and CNNs.CXR and CT images were employed in this study to detect illnesses linked to COVID19 [24] , [25] , [26] , [27] , [28] .…”

Section: Related Workmentioning

confidence: 99%

Detecting Covid19 and pneumonia from chest X-ray images using deep convolutional neural networks

Kavya

shilpa

Veeranjaneyulu

et al. 2022

Materials Today: Proceedings

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Section: Related Workmentioning

confidence: 99%

Detecting Covid19 and pneumonia from chest X-ray images using deep convolutional neural networks

Kavya

shilpa

Veeranjaneyulu

et al. 2022

Materials Today: Proceedings

Self Cite

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“…Recently, several deep learning architectures for FER have emerged. Bodapati et al [3] proposed a deep convolutional neural network model (FerNet). This model comprises a sequence of blocks consisting of multiple convolutional and sub-sampling layers.…”

Section: Facial Expression Recognition Systemsmentioning

confidence: 99%

“…The former provides a CNN model trained with 48x48 pixel samples. In contrast, the latter provides four models, two handcrafted models, random forest (RF) and support-vector machine (SVM), and two deep learning models, FerNet [3] and ResMaskNet [29].…”

Section: Face Processing Pipelinementioning

confidence: 99%

Facial expression analysis in a wild sporting environment

Santana

Freire-Obregón

Hernández-Sosa

et al. 2022

Multimed Tools Appl

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The scientific community and mass media have already reported the use of nonverbal behavior analysis in sports for athletes’ performance. Their conclusions stated that certain emotional expressions are linked to athlete’s performance, or even that psychological strategies serve to improve endurance performance. This paper examines the portrayal of well-known emotions and their relationship to the participants of an ultra-distance race in a high-stake environment. For this purpose, we analyzed almost 600 runners captured when they passed through a set of locations placed along the race track. We have observed a correlation between the runners’ facial expressions and their performance along the track. Moreover, we have analyzed Action Unit activations and aligned our findings with the state-of-the-art psychological baseline.

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“…The proposed method in the researches [ 33 ] is based on the twin delayed deep deterministic policy gradient algorithm, also known as the value-based deep reinforcement learning algorithm (TD3). The investigator [ 34 ] offers a unique deep learning-based technique to handle the difficulties of face emotion identification from pictures. In order to aid researchers in creating effective strategies for ECG signal processing, this work [ 35 ] provides a comprehensive assessment of feature extraction techniques.…”

Section: Introductionmentioning

confidence: 99%

A Modified Artificial Neural Network (ANN)-Based Time Series Prediction of COVID-19 Cases from Multi-Country Data

Majhi

2023

J. Inst. Eng. India Ser. B

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The deadly Corona virus that first appeared in a seafood market in the Wuhan city of China in December 2019 has been causing global distress by claiming lives and collapsing economies. Given its serious nature, there is an urgent need to understand the virus’s future trajectory. The current study predicts the next day confirmed, death and recovery cases of COVID-19 pandemic for India, Italy, Spain, and the USA by using a modified multilayer neural network (MMLNN) model. The spread of the COVID-19 data is collected from the Kaggle website for the period of 22nd January 2020 to 20th April 2020 (i.e., for 90 days). The predicted figures of the spread of the disease have been estimated and compared with the actual values. Higher precision of the estimates has been observed from the MMLNN model compared to the conventional multilayer neural network (MLANN) model. Specifically, the MMLNN model does faster and more efficient training of the data resulting in less error. The paper forecasts the next day figures (i.e., for 21st April) for all the three cases and does the comparison of the results with the actual values reported. A deviation of 6% is obtained for India, and for the other three countries the deviation is below 3.5%. Given the high accuracy predictive power, the authors recommend that the MMLNN model can be integrated into the health policy of the countries that are struggling with the spread of the virus. Specifically, a decision on health policies such as restrictions on movement can be based on the short-range predictions of the spread of the virus infection.

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FERNet: A Deep CNN Architecture for Facial Expression Recognition in the Wild

Cited by 32 publications

References 23 publications

Detecting Covid19 and pneumonia from chest X-ray images using deep convolutional neural networks

Detecting Covid19 and pneumonia from chest X-ray images using deep convolutional neural networks

Facial expression analysis in a wild sporting environment

A Modified Artificial Neural Network (ANN)-Based Time Series Prediction of COVID-19 Cases from Multi-Country Data

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