Facial Expression Recognition via Deep Action Units Graph Network Based on Psychological Mechanism

Liu, Yang; Zhang, Xingming; Lin, Yubei; Wang, Haoxiang

doi:10.1109/tcds.2019.2917711

Cited by 51 publications

(29 citation statements)

References 31 publications

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“…Recent studies reveal that introducing knowledge of the multi-label space can alleviate the effect of ambiguous facial expressions [12], [19]. In this work, we choose AU detection as our auxiliary task and construct semantically representative AU graphs because the Facial Action Coding System is an affect description model that has latent mappings with expression categories [20], [21], [22]. The AU graph takes individual AU features as graph nodes and the co-occurring AU dependency as graph edges.…”

Section: Auxiliary Au Graph Branchmentioning

confidence: 99%

Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition

Liu¹,

Zhang²,

Kauttonen³

et al. 2022

Preprint

Self Cite

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High-quality annotated images are significant to deep facial expression recognition (FER) methods. However, uncertain labels, mostly existing in large-scale public datasets, often mislead the training process. In this paper, we achieve uncertain label correction of facial expressions using auxiliary action unit (AU) graphs, called ULC-AG. Specifically, a weighted regularization module is introduced to highlight valid samples and suppress category imbalance in every batch. Based on the latent dependency between emotions and AUs, an auxiliary branch using graph convolutional layers is added to extract the semantic information from graph topologies. Finally, a re-labeling strategy corrects the ambiguous annotations by comparing their feature similarities with semantic templates. Experiments show that our ULC-AG achieves 89.31% and 61.57% accuracy on RAF-DB and AffectNet datasets, respectively, outperforming the baseline and state-of-the-art methods.

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Section: Auxiliary Au Graph Branchmentioning

confidence: 99%

Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition

Liu¹,

Zhang²,

Kauttonen³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [68], an attentional DCNN named a deep emotion to tackle the FER problem was devised. In [69], a deep AU graph network was presented based on a psychological mechanism. In the first step, the face image is divided into small key areas using segmentation techniques.…”

Section: Related Workmentioning

confidence: 99%

Facial Expression Recognition Using Local Gravitational Force Descriptor-Based Deep Convolution Neural Networks

Karnati

Seal

Krejcar

et al. 2021

IEEE Trans. Instrum. Meas.

112

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An image is worth a thousand words; hence, a face image illustrates extensive details about the specification, gender, age, and emotional states of mind. Facial expressions play an important role in community-based interactions and are often used in the behavioral analysis of emotions. Recognition of automatic facial expressions from a facial image is a challenging task in the computer vision community and admits a large set of applications, such as driver safety, human-computer interactions, health care, behavioral science, video conferencing, cognitive science, and others. In this work, a deep-learning-based scheme is proposed for identifying the facial expression of a person. The proposed method consists of two parts. The former one finds out local features from face images using a local gravitational force descriptor, while, in the latter part, the descriptor is fed into a novel deep convolution neural network (DCNN) model. The proposed DCNN has two branches. The first branch explores geometric features, such as edges, curves, and lines, whereas holistic features are extracted by the second branch. Finally, the scorelevel fusion technique is adopted to compute the final classification score. The proposed method along with 25 state-of-the-art methods is implemented on five benchmark available databases,

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“…A deep neural network (DNN) is powerful for extracting rich hierarchical feature representations [1,2]. The superiority of feature extraction helps DNN based approaches to make compelling achievement on semantic segmentation.…”

Section: Introductionmentioning

confidence: 99%

Improving Synthetic to Realistic Semantic Segmentation With Parallel Generative Ensembles for Autonomous Urban Driving

Fang

Hua

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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Semantic segmentation is paramount for autonomous vehicles to have a deeper understanding of the surrounding traffic environment and enhance safety. Deep neural networks (DNN) have achieved remarkable performances in semantic segmentation. However, training such a DNN requires a large amount of labelled data at pixel level. In practice, it is a labour-intensive task to manually annotate dense pixel-level labels. To tackle the problem associated with a small amount of labelled data, Deep Domain Adaptation (DDA) methods have recently been developed to examine the use of synthetic driving scenes so as to significantly reduce the manual annotation cost. Despite remarkable advances, these methods unfortunately suffer from the generalisability problem that fails to provide a holistic representation of the mapping from the source image domain to the target image domain. In this paper, we therefore develop a novel ensembled DDA to train models with different upsampling strategies, discrepancy and segmentation loss functions. The models are, therefore, complementary with each other to achieve better generalisation in the target image domain. Such a design does not only improve the adapted semantic segmentation performance, but also strengthen the model reliability and robustness. Extensive experimental results demonstrate the superiorities of our approach over several state-of-the-art methods.

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Facial Expression Recognition via Deep Action Units Graph Network Based on Psychological Mechanism

Cited by 51 publications

References 31 publications

Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition

Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition

Facial Expression Recognition Using Local Gravitational Force Descriptor-Based Deep Convolution Neural Networks

Improving Synthetic to Realistic Semantic Segmentation With Parallel Generative Ensembles for Autonomous Urban Driving

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