Deep Facial Expression Recognition: A Survey
Shan Li,Weihong Deng +1 more
TLDR
A comprehensive survey on deep facial expression recognition (FER) can be found in this article, including datasets and algorithms that provide insights into the intrinsic problems of deep FER, including overfitting caused by lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias.Abstract:
With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. Recent deep FER systems generally focus on two important issues: overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias. In this paper, we provide a comprehensive survey on deep FER, including datasets and algorithms that provide insights into these intrinsic problems. First, we describe the standard pipeline of a deep FER system with the related background knowledge and suggestions of applicable implementations for each stage. We then introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets. For the state of the art in deep FER, we review existing novel deep neural networks and related training strategies that are designed for FER based on both static images and dynamic image sequences, and discuss their advantages and limitations. Competitive performances on widely used benchmarks are also summarized in this section. We then extend our survey to additional related issues and application scenarios. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust deep FER systems.read more
Citations
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Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
TL;DR: A deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE is proposed.
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Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
TL;DR: A novel Region Attention Network (RAN), to adaptively capture the importance of facial regions for occlusion and pose variant FER, and a region biased loss to encourage high attention weights for the most important regions.
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Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition
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Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy
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TL;DR: This work proposes two novel CNN architectures which achieve a human-like accuracy of 65% and can serve as a basis for standardization of the base model for the much inquired FER-2013 dataset.
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TL;DR: A training strategy that treats the head data and the tail data in an unequal way, accompanying with noise-robust loss functions, to take full advantage of their respective characteristics and achieve the best result on MegaFace Challenge 2 given a large-scale noisy training data set is proposed.
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