Novel multi-scale deep residual attention network for facial expression recognition

Novel multi-scale deep residual attention network for facial expression recognition

Recently, the deep convolutional neural networks (CNNs) have shown great success and for facial expression recognition (FER). These CNN-based approaches have made breakthroughs in the accuracy by using deeper networks. As the amount of train data is very less, these models easily fall into the overf...

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Bibliographic Details
Main Authors: Dong Liu, Lifeng Wang, Zhiyong Wang, Longxi Chen
Format: Article
Language:English
Published: Wiley 2020-12-01
Series:The Journal of Engineering
Subjects:
face recognition
learning (artificial intelligence)
feature extraction
image classification
emotion recognition
neural nets
different scale sub-units
parametric rectified linear units
residual attention connection
different channel features
ms-ran
informative channel features
exiting network
multiscale structure
residual attention mechanism
original facial expression image
novel multiscale deep residual attention network
facial expression recognition
deep convolutional neural networks
deeper networks
multiscale residual attention unit
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2020.0183

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https://digital-library.theiet.org/content/journals/10.1049/joe.2020.0183

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