Fast and Efficient Facial Expression Recognition Using a Gabor Convolutional Network

Jiang, Ping; Wan, Bo; Wang, Quan; Wu, Jiang

doi:10.1109/lsp.2020.3031504

Cited by 34 publications

(18 citation statements)

References 32 publications

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“…Liu et al [ 32 ] designed a Point Adversarial Self Mining (PASM) model, and then they utilized a point adversary self-mined network to enhance data and teacher–student pattern to train recognition networks. Jiang et al [ 33 ] applied Gabor convolutional network [ 34 ] to the field of expression recognition, and obtained an efficient and fast model.…”

Section: Related Workmentioning

confidence: 99%

FERGCN: facial expression recognition based on graph convolution network

Liao

Zhu

Zheng

et al. 2022

Machine Vision and Applications

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Due to the problems of occlusion, pose change, illumination change, and image blur in the wild facial expression dataset, it is a challenging computer vision problem to recognize facial expressions in a complex environment. To solve this problem, this paper proposes a deep neural network called facial expression recognition based on graph convolution network (FERGCN), which can effectively extract expression information from the face in a complex environment. The proposed FERGCN includes three essential parts. First, a feature extraction module is designed to obtain the global feature vectors from convolutional neural networks branch with triplet attention and the local feature vectors from key point-guided attention branch. Then, the proposed graph convolutional network uses the correlation between global features and local features to enhance the expression information of the non-occluded part, based on the topology graph of key points. Furthermore, the graph-matching module uses the similarity between images to enhance the network’s ability to distinguish different expressions. Results on public datasets show that our FERGCN can effectively recognize facial expressions in real environment, with RAF-DB of 88.23%, SFEW of 56.15% and AffectNet of 62.03%.

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Section: Related Workmentioning

confidence: 99%

FERGCN: facial expression recognition based on graph convolution network

Liao

Zhu

Zheng

et al. 2022

Machine Vision and Applications

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show abstract

“…This approach has proven to enhance the recognition performance with a perceptual reduction in the architecture size. Motivated by [19] with varying depth for fast and efficient facial expression recognition [20]. In order to extract distinctive feature at different scales and orientations from limited training data, a combination of both fixed Gabor ensemble filter and AWFs were proposed for hyperspectral image classification [21].…”

Section: Related Work a Gabor-based Cnnsmentioning

confidence: 99%

Convolutional Neural Network Based on Diverse Gabor Filters for Deepfake Recognition

et al. 2022

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Media synthesis and manipulation has reached unprecedented levels of realism owing to the proliferation of deep learning. Deepfake has been the de-facto tool for media manipulation. Although this technology has potential in the entertainment industry, its threats include political manipulation and bypassing biometric security systems. As a result, deepfake detection has garnered widespread attention among research communities. The intuition is to use deep learning to fix the problems created by deep learning. Although convolutional neural networks have shown their dominance in the filed of pattern recognition, the receptive field-model size dilemma still persists along with the lack of interpretation for such models. While the traditional Gabor function was proposed to fix these problems, it can only generate limited linear Gabor filters which makes it optimal for limited data and applications. The contribution of this paper is quadruple: (i) proposing a unified Gabor function capable of generating linear, elliptical, and circular Gabor filters. (ii) leveraging the back-propagation learning framework to incorporate the proposed function in convolutional neural networks and generate adaptive Gabor filters. (iii) presenting a dual scale large receptive field network for deepfake image recognition. (iv) demonstrating where the proposed model stands in terms of performance and architecture size compared to state-of-the-art models. The proposed model is evaluated on four benchmark datasets: Celeb-DF (v2), DeepFake Detection Challenge Preview, FaceForensics++ and Wilddeepfake. Experimental results show that the proposed adaptive Gabor filters reduces the model size by 64.9% compared to adaptive weighted filters without performance reduction.

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“…Recent researches demonstrate that ME analysis has potential and emerging applications in different fields, such as clinical diagnosis, national security and interrogations [6,25]. However, direct ME recognition can be very challenging because of ambiguities between several MEs [3,24,12]. One of the effective methods in resolving the ambiguity issue is employing the Facial Action Coding System (FACS) to represent individual expressions [7].…”

Section: Introductionmentioning

confidence: 99%

Intra- and Inter-Contrastive Learning for Micro-expression Action Unit Detection

Zhao

2021

Proceedings of the 2021 International Conference on Multimodal Interaction

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Encoding facial expressions via Action Units (AUs) has been found effective for resolving the ambiguity issue among different expressions. In the literature, AU detection has extensive researches in macro-expressions. However, there is limited research about AU analysis for microexpressions (MEs). ME AU detection becomes a challenging problem because of the subtle facial motion. To alleviate this problem, in this paper, we study the contrastive learning for modeling subtle AUs and propose a novel ME AU detection method by learning the intra-and intercontrastive information among MEs. Through the intracontrastive learning module, the difference between the onset and apex frames is enlarged and utilized to obtain the discriminative representation for low-intensity AU detection. In addition, considering the subtle difference between ME AUs, the inter-contrastive learning is designed to automatically explore and enlarge the difference between different AUs to enhance the ME AU detection robustness. Intensive experiments on two widely used ME databases have demonstrated the effectiveness and generalization ability of our proposed method.

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Fast and Efficient Facial Expression Recognition Using a Gabor Convolutional Network

Cited by 34 publications

References 32 publications

FERGCN: facial expression recognition based on graph convolution network

FERGCN: facial expression recognition based on graph convolution network

Convolutional Neural Network Based on Diverse Gabor Filters for Deepfake Recognition

Intra- and Inter-Contrastive Learning for Micro-expression Action Unit Detection

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