Proceedings ArticleDOI
Identity-Aware Convolutional Neural Network for Facial Expression Recognition
Zibo Meng,Ping Liu,Jie Cai,Shizhong Han,Yan Tong +4 more
- pp 558-565
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TLDR
An identity-sensitive contrastive loss is proposed to learn identity-related information from identitylabels to achieve identity-invariant expression recognition in a novel identity-aware convolutional neural network.Abstract:
Facial expression recognition suffers under realworldconditions, especially on unseen subjects due to highinter-subject variations. To alleviate variations introduced bypersonal attributes and achieve better facial expression recognitionperformance, a novel identity-aware convolutional neuralnetwork (IACNN) is proposed. In particular, a CNN with a newarchitecture is employed as individual streams of a bi-streamidentity-aware network. An expression-sensitive contrastive lossis developed to measure the expression similarity to ensure thefeatures learned by the network are invariant to expressionvariations. More importantly, an identity-sensitive contrastiveloss is proposed to learn identity-related information from identitylabels to achieve identity-invariant expression recognition.Extensive experiments on three public databases including aspontaneous facial expression database have shown that theproposed IACNN achieves promising results in real world.read more
Citations
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Journal ArticleDOI
Deep Facial Expression Recognition: A Survey
Shan Li,Weihong Deng +1 more
TL;DR: 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.
Proceedings ArticleDOI
Facial Expression Recognition by De-expression Residue Learning
TL;DR: The DeRL method has been evaluated on five databases, CK+, Oulu-CASIA, MMI, BU-3DFE, and BP4D+.
Journal ArticleDOI
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
TL;DR: Zhang et al. as mentioned in this paper proposed a region attention network (RAN) to adaptively capture the importance of facial regions for occlusion and pose variant FER by aggregating and embedding varied number of region features produced by a backbone convolutional neural network into a compact fixed-length representation.
Posted Content
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.
Proceedings ArticleDOI
Island Loss for Learning Discriminative Features in Facial Expression Recognition
TL;DR: Experimental results on four benchmark expression databases have demonstrated that the CNN with the proposed island loss (IL-CNN) outperforms the baseline CNN models with either traditional softmax loss or center loss and achieves comparable or better performance compared with the state-of-the-art methods for facial expression recognition.
References
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