Discriminative Attention-based Convolutional Neural Network for 3D Facial Expression Recognition

Zhu, Kangkang; Du, Zhengyin; Li, Weixin; Huang, Di; Wang, Yunlong; Chen, Liming

doi:10.1109/fg.2019.8756524

Cited by 31 publications

(16 citation statements)

References 30 publications

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“…In addition, we suggest a novel DA strategy based on the non rigid CPD registration, hence generating new additional realistic 3D facial expressions that allow us to augment the initial size of the BU-3DFE database. We have reported an encouraging average recognition rate of 97.23% overcoming most of state-of-art works [30,[32][33][34].…”

Section: Discussionmentioning

confidence: 86%

“…Furthermore, most literature work, as in Ref. [7,9,12,[31][32][33][34], has exploited only the two highest levels of the BU-3DFE database due to the fact that weak expressions (lower levels) are hardly recognized. On the other hand, in our approach we exploit the four different levels of BU-3DFE, which improves the recognition of weak expressions.…”

Section: Cpd-based Damentioning

confidence: 99%

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Facial Expression Recognition Using 3D Points Aware Deep Neural Network

Trimech¹,

Maalej²,

Amara³

2021

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Point cloud-based Deep Neural Networks (DNNs) have gained increasing attention as an insightful solution in the study field of geometric deep learning. Point set aware DNNs have proven capable of dealing with the unstructured data type and successful in 3D data applications such as 3D object classification, segmentation and recognition. On the other hand, two major challenges remain understudied when it comes to the use of point cloud-based DNNs for 3D facial expression (FE) recognition. The first challenge is the lack of large labelled 3D facial data. The second is how to obtain a point-based discriminative representation of 3D faces. To address the first issue, we suggest to enlarge the used dataset by generating synthetic 3D FEs. For the second one, we propose to apply a level-curve based sampling strategy in order to exploit crucial geometric information. The conducted experiments show promising results reaching 97.23% on the enlarged BU-3DFE dataset.

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Section: Discussionmentioning

confidence: 86%

Section: Cpd-based Damentioning

confidence: 99%

Facial Expression Recognition Using 3D Points Aware Deep Neural Network

Trimech¹,

Maalej²,

Amara³

2021

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“…Some of the last year's works are [112][113][114][115][116][117]. Others are those of Jan et al [51] and Zhu et al [118]. Jan et al [51] designed a novel system for 3D FER based on accurate facial parts extraction according to localized facial landmarks, and deep feature fusion of facial parts.…”

Section: B 3d Fermentioning

confidence: 99%

“…Finally, a multi-class SVM classifier is adopted for facial expression prediction. Zhu et al [118] introduced a novel deep learning approach to 3D FER, namely Discriminative Attention-based Convolution Neural Network (DA-CNN), to capture more comprehensive expression related representations. They conducted several experiments to prove the effectiveness of their method, and state-of-art results are achieved for both 3D FER and multi-modal 3D+2D FER.…”

Section: B 3d Fermentioning

confidence: 99%

3D Approaches and Challenges in Facial Expression Recognition Algorithms—A Literature Review

et al. 2019

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In recent years, facial expression analysis and recognition (FER) have emerged as an active research topic with applications in several different areas, including the human-computer interaction domain. Solutions based on 2D models are not entirely satisfactory for real-world applications, as they present some problems of pose variations and illumination related to the nature of the data. Thanks to technological development, 3D facial data, both still images and video sequences, have become increasingly used to improve the accuracy of FER systems. Despite the advance in 3D algorithms, these solutions still have some drawbacks that make pure three-dimensional techniques convenient only for a set of specific applications; a viable solution to overcome such limitations is adopting a multimodal 2D+3D analysis. In this paper, we analyze the limits and strengths of traditional and deep-learning FER techniques, intending to provide the research community an overview of the results obtained looking to the next future. Furthermore, we describe in detail the most used databases to address the problem of facial expressions and emotions, highlighting the results obtained by the various authors. The different techniques used are compared, and some conclusions are drawn concerning the best recognition rates achieved.

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“…Li proposed Deep Locality-Preserving CNN (DLP-CNN) to recognize facial expressions in the wild [3], The disadvantage of the above-mentioned methods is that the features they extracted are lessdiscriminative because of equal weights for informative regions (or channels) and non-informative regions (or channels). Motivated by the perspective that not all regions and channels contain useful information for expression recognition [39] [41], we think that attention mechanism is an effective solution to highlight useful region and suppress other regions' interferences.…”

Section: Introductionmentioning

confidence: 99%

Information Reuse Attention in Convolutional Neural Networks for Facial Expression Recognition in the Wild

Wang

2021

2021 International Joint Conference on Neural Networks (IJCNN)

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Unlike the constraint frontal face condition, faces in the wild have various unconstrained interference factors, such as complex illumination, changing perspective and various occlusions. Facial expressions recognition (FER) in the wild is a challenging task and existing methods can't perform well. However, for occluded faces (containing occlusion caused by other objects and self-occlusion caused by head posture changes), the attention mechanism has the ability to focus on the nonoccluded regions automatically. In this paper, we propose a Lossless Attention Model (LLAM) for convolutional neural networks (CNN) to extract attention-aware features from faces. Our module avoids decay information in the process of generating attention maps by using the information of the previous layer and not reducing the dimensionality. Sequentially, we adaptively refine the feature responses by fusing the attention map with the feature map. We participate in the seven basic expression classification sub-challenges of FG-2020 Affective Behavior Analysis in-thewild Challenge. And we validate our method on the Aff-Wild2 datasets released by the Challenge. The total accuracy (Accuracy)and the unweighted mean (F1) of our method on the validation set are 0.49 and 0.38 respectively, and the final result is 0.42 (0.67 F1_Score + 0.33 Accuracy).

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Discriminative Attention-based Convolutional Neural Network for 3D Facial Expression Recognition

Cited by 31 publications

References 30 publications

Facial Expression Recognition Using 3D Points Aware Deep Neural Network

Facial Expression Recognition Using 3D Points Aware Deep Neural Network

3D Approaches and Challenges in Facial Expression Recognition Algorithms—A Literature Review

Information Reuse Attention in Convolutional Neural Networks for Facial Expression Recognition in the Wild

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